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1
ATHANASIOU, THOMAS. "Multi-dimensional analysis of social multi-networks : Analysing a 5-layer social network case study." Thesis, Uppsala universitet, Institutionen för informatik och media, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273908.
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Network theory analysis is applicable to many scientific disciplines (fields) such as biology, statistics and sociology. The social network analysis is one of the various branches of the broader network theory analysis, the social network analysis. It is of high interest among the researchers in social sciences. Social networks have had a significant impact on human civilizations for many centuries. During the last two decades, the main academic interest was addressed towards the research and analysis of a dynamically uprising sector of social networks, the on-line networks, primarily due to the domination of the Internet and technology over human attitudes and relations in modern societies. For many years, research was emphasized on the analysis of simple social networks, whilst during the last decade several researchers started working on the analysis of more complicated social networks, which consist by several smaller social networks. There are important differences between mono and multi-dimensional network analysis. Mono-dimensional analysis provides the research with relevant knowledge. On the other hand, multi-dimensional analysis is still at initial stage. As a result, several potential models related to the multi-networks analysis cannot always provide reliable and adequate outcomes. However, due to the fact that different social networks can be easily combined and form more extended and complicated networks, it is of high importance for the researchers to advance the multi-dimensional analysis and provide more adequate analytical models. The purpose of this thesis is to present the dynamic of the multi-dimensional analysis by consecutively applying both mono and multi-dimensional analysis on a social multi-network. The findings suggest that multi-dimensional analysis can add reliable knowledge on the social network analysis, but many problems that arose due the complexity of the multi-networks structures need to be addressed.
2
Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
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Vetro, Carla. "La social network analysis nella valutazione delle politiche sociali." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/341.
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2009 - 2010<br>Il tema della valutazione emerge periodicamente nella discussione politica italiana. L’azione del
valutare, che rappresenta ormai un’operazione ricorrente nella vita quotidiana, diviene una pratica
consolidata anche in seno alle istituzioni pubbliche, indispensabile per costruire un giudizio sul
funzionamento delle politiche stesse. La pratica valutativa si rivela, però, difficile da applicare in
contesti complessi e dinamici come quelli che caratterizzano gli interventi nel sociale, dove la
complessità attiene alla eterogeneità e pluralità di attori coinvolti e alla multiproblematicità dei
bisogni territoriali. Quando la riuscita di una politica di intervento dipende non solo dalle capacità
di coordinamento dall’alto, cioè di chi programma gli interventi sociali e offre i servizi per
rispondere ai bisogni di una comunità, ma anche dalla volontà e dalla partecipazione dal basso, cioè
di chi fruisce degli interventi, risulta chiaro quanto un processo di valutazione diventi complesso.
In tali situazioni, le tecniche della Social Network Analysis (di seguito analisi delle reti sociali)
risultano particolarmente adatte a rilevare, studiare ed interpretare le interazioni di tutti gli attori
coinvolti in uno o più interventi di politica sociale. Tali tecniche di analisi vengono utilizzate
sempre più spesso nella ricerca valutativa, in quanto si presuppone che ci possa essere una relazione
fra le caratteristiche della rete, costituita dagli attori sociali coinvolti nell’attuazione di un
programma, e l’efficacia del programma stesso. [a cura dell'autore]<br>IX n.s.
4
CURZI, MIRCO. "Content based social network analysis." Doctoral thesis, Università Politecnica delle Marche, 2009. http://hdl.handle.net/11566/242305.
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Moore, John David. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/103640.
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Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.<br>Doctor of Philosophy<br>Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
6
Moore, John. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103640.
Pełny tekst źródłaStreszczenie:
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.<br>Doctor of Philosophy<br>Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005).
The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network.
The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants.
The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations.
After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions.
I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously.
Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
7
Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.
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As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding the importance of each individual subscriber in the network. This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks. Another important part of using social network analysis is to be able to interpret the results. This can be cumbersome without expert knowledge. For that reason, a complete process flow for finding influential subscribers in a telecommunication network has been developed. The flow uses input easily available to the telecommunication operator. In addition to using social network analysis, machine learning is employed to uncover what behavior is associated with influence and pinpointing subscribers behaving accordingly.
8
Broccatelli, Chiara. "Going beyond secrecy : methodological advances for two-mode temporal criminal networks with Social Network Analysis." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/going-beyond-secrecy-methodological-advances-for-twomode-temporal-criminal-networks-with-social-network-analysis(f0f91f79-7bc3-442c-a16b-e9cf44cc68c3).html.
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This thesis seeks to extend the application of Social Network Analysis (SNA) to temporal graphs, in particular providing new insights for the understanding of covert networks. The analyses undertaken reveal informative features and properties of individuals' affiliations under covertness that also illustrate how both individuals and events influence the network structure. The review of the literature on covert networks provided in the initial two chapters suggests the presence of some ambiguities concerning how authors define structural properties and dynamics of covert networks. Authors sometimes disagree and use their findings to explain opposite views about covert networks. The controversy in the field is used as a starting point in order to justify the methodological application of SNA to understand how individuals involved in criminal and illegal activities interact with each other. I attempt to use a deductive approach, without preconceived notions about covert network characteristics. In particular, I avoid considering covert networks as organisations in themselves or as cohesive groups. I focus on individuals and their linkages constructed from their common participation in illicit events such as secret meetings, bombing attacks and criminal operations. In order to tackle these processes I developed innovative methods for investigating criminals' behaviours over time and their willingness to exchange tacit information. The strategy implies the formulation of a network model in order to represent and incorporate in a graph three types of information: individuals, events, and the temporal dimension of events. The inclusion of the temporal dimension offers the possibility of adopting a more comprehensive theoretical framework for considering individuals and event affiliations. This thesis expands the analysis of bipartite covert networks by adopting several avenues to explore in this perspective. Chapter 3 proposes a different way to represent two-mode networks starting from the use of line-graphs, namely the bi-dynamic line-graph data representation (BDLG), through which it is possible to represent the temporal evolution of individual's trajectories. The following chapter 4 presents some reflections about the idea of cohesion and cohesive subgroups specific to the case of two-mode networks. Based on the affiliation matrices, the analysis of local clustering through bi-cliques offers an attempt to analyse the mechanism of selecting accomplices while taking into account time. Chapter 5 is concerned with the concept of centrality of individuals involved in flows of knowledge exchanges. The theoretical and analytical framework helps in elaborating how individuals share their acquired hands-on experiences with others by attending joint task activities over time. Chapter 6 provides an application of the approaches introduced in the preceding chapters to the specific case of the Noordin Top terrorist network. Here, the knowledge of experience flow centrality measure opens up a new way to quantify the transmission of information and investigate the formation of the criminal capital. Finally, the last Chapter 7 presents some future research extensions by illustrating the versatility of the proposed approaches in order to provide new insights for the understanding of criminals' behaviours.
9
Isah, Haruna. "Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.
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With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.
10
Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.
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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação<br>This research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA).
Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon.
The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market.
Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA.
In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed.
We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
11
Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161.
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Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.
12
Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A27871.
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Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.
13
Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.
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This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below.
1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in scholarly publications. Less frequently used are joint grant proposals and patents. Many scholars believe that co-authorship as the sole measure of research collaboration is insufficient because collaboration between researchers might not result in co-authorship. Collaborations involve informal communication (i.e., conversational exchange) between researchers. Using self-reports from 100 tenured/tenure-track faculty in the College of Engineering at the University of South Florida, researchers' networks are constructed from their communication relations and collaborations in three areas: joint publications, joint grant proposals, and joint patents. The data collection: 1) provides a rich data set of both researchers' in-progress and completed collaborative outputs, 2) yields a rating from the researchers on the importance of a tie to them 3) obtains multiple types of ties between researchers allowing for the comparison of their multiple networks. Exponential Random Graph Model (ERGM) results show that the more communication researchers have the more likely they produce collaborative outputs. Furthermore, the impact of four demographic attributes: gender, race, department affiliation, and spatial proximity on collaborative output relations is tested. The results indicate that grant proposals are submitted with mixed gender teams in the college of engineering. Besides, the same race researchers are more likely to publish together. The demographics do not have an additional leverage on joint patents.
2. Study II: Previous research shows that researchers' social network metrics obtained from a collaborative output network (e.g., joint publications or co-authorship network) impact their performance determined by g-index. This study uses a richer dataset to show that a scholar's performance should be considered with respect to position in multiple networks. Previous research using only the network of researchers' joint publications shows that a researcher's distinct connections to other researchers (i.e., degree centrality), a researcher's number of repeated collaborative outputs (i.e., average tie strength), and a researchers' redundant connections to a group of researchers who are themselves well-connected (i.e., efficiency coefficient) has a positive impact on the researchers' performance, while a researcher's tendency to connect with other researchers who are themselves well-connected (i.e., eigenvector centrality) had a negative impact on the researchers' performance. The findings of this study are similar except that eigenvector centrality has a positive impact on the performance of scholars. Moreover, the results demonstrate that a researcher's tendency towards dense local neighborhoods (as measured by the local clustering coefficient) and the researchers' demographic attributes such as gender should also be considered when investigating the impact of the social network metrics on the performance of researchers.
3. Study III: This study investigates to what extent researchers' interactions in the early stage of their collaborative network activities impact the number of collaborative outputs produced (e.g., joint publications, joint grant proposals, and joint patents). Path models using the Partial Least Squares (PLS) method are run to test the extent to which researchers' individual innovativeness, as determined by the specific indicators obtained from their interactions in the early stage of their collaborative network activities, impacts the number of collaborative outputs they produced taking into account the tie strength of a researcher to other conversational partners (TS). Within a college of engineering, it is found that researchers' individual innovativeness positively impacts the volume of their collaborative outputs. It is observed that TS positively impacts researchers' individual innovativeness, whereas TS negatively impacts researchers' volume of collaborative outputs. Furthermore, TS negatively impacts the relationship between researchers' individual innovativeness and the volume of their collaborative outputs, which is consistent with `Strength of Weak Ties' Theory. The results of this study contribute to the literature regarding the transformation of tacit knowledge into explicit knowledge in a university context.
14
Franco, Alessia <1996>. "How Blockchain Technology Can Help Rearchitect Social Networks: An Analysis of Desmos Network." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19800.
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Centralized Social Networks (CSNs) such as Facebook, Instagram and Twitter are operated in an obscure, opaque and autocratic manner; many problems arise from centralized corporate power, these issues are based on the misalignment between profit-seeking incentives of corporations and user goals. Censorship, banning and data breaches have become increasingly frequent in the past five years, recent political tensions and the outbreak of Covid-19 have worsened this situation globally.
The main issue has an inborn nature: the business model of CSNs utilizes and monetizes users’ data to maximize the profitability of tech giants. In this thesis I explore the case of Desmos, which proposes an alternative approach to social media creation and management. Based on the belief that that the root cause of this problem is the broken relationship between the different parties, Desmos offers a project to fix this broken relationship using blockchain technology. Through a redesigned lifecycle and an improved economic model it can achieve the network growth required for a self-sustainable social network, without the need for centralized intermediaries, thus prioritizing the interests of users. Desmos is therefore the protocol that can be used to build decentralized social networks, while Desmos Token (DSM) is the native token of this blockchain, which allows the holders to contribute to the security and governance of the platform in exchange for incentives.
15
Gamal, Doaa. "Social Networks Influence Analysis." UNF Digital Commons, 2017. http://digitalcommons.unf.edu/etd/723.
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Pew Research Center estimates that as of 2014, 74% of the Internet Users used social media, i.e., more than 2.4 billion users. With the growing popularity of social media where Internet users exchange their opinions on many things including their daily life encounters, it is not surprising that many organizations are interested in learning what users say about their products and services. To be able to play a proactive role in steering what user’s say, many organizations have engaged in efforts aiming at identifying efficient ways of marketing certain products and services, and making sure user reviews are somewhat favorable. Favorable reviews are typically achieved through identifying users on social networks who have a strong influence power over a large number of other users, i.e. influential users.
Twitter has emerged as one of the prominent social network services with 320 million monthly active users worldwide. Based on the literature, influential Twitter users have been typically analyzed using the following three models: topic-based model, topology-based model, and user characteristics-based model. The topology-based model is criticized for being static, i.e., it does not adapt to the social network changes such as user’s new posts, or new relationships. The user characteristics-based model was presented as an alternative approach; however, it was criticized for discounting the impact of interactions between users, and users’ interests. Lastly, the topic-based model, while sensitive to users’ interests, typically suffers from ignoring the inclusion of inter-user interactions.
This thesis research introduces a dynamic, comprehensive and topic-sensitive approach for identifying social network influencers leveraging the strengths of the aforementioned models. Three separate experiments were conducted to evaluate the new approach using the information diffusion measure. In these experiments, software was developed to capture users’ tweets pertinent to a topic over a period of time, and store the tweet’s metadata in a relational database. A graph representing users was extracted from the database. The new approach was applied to the users’ graph to compute an influence score for each user.
Results show that the new composite influence score is more accurate in comprehensively identifying true influential users, when compared to scores calculated using the characteristics-based, topic-based, and topology-based models. Also, this research shows that the new approach could leverage a variety of machine learning algorithms to accurately identify influencers.
Last, while the focus of this research was on Twitter, our approach may be applicable to other social networks and micro-blogging services.
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Stearmer, Steven Matthew Ph D. "Diaspora Social Movements in Cyberspace: Epistemological and Ethnographic Considerations." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1460658606.
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Kochar, Shilpa. "Network ties and their effect on employee collaboration in enterprise social networks." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/210864/1/Shilpa_Kochar_Thesis.pdf.
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There has been a rapid growth and widespread adoption of social media technologies across all industries. Despite the growing importance of enterprise social networks (ESN), there has been limited research in examining the role of employee relationships (ties) in these networks. To gain an in-depth understanding of ties and collaboration outcomes, a mixed method research was conducted. StackExchange data was collected and processed, social network analysis and qualitative analysis of data has been done and the findings are presented in the form of an empirically derived theoretical model. Study provides novel insights into importance of negative ties and reciprocal ties.
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Hildorsson, Fredrik. "Scalable Solutions for Social Network Analysis." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-110548.
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<p>A telecom operator can get a lot of high quality intelligence by studying the social network of its subscribers. One way to generate such a social network is to study the calls between the subscribers. Social networks generated from telecom networks can consist of millions of subscribers and the majority of the current social network analysis algorithms are too slow to analyze large networks. This master's thesis' objective is to find a more scalable solution to analyze social networks.</p><p>The work was divided into three steps; a survey of the existing solutions and algorithms, a pre-study to verify limitations of existing solutions and test some ideas and from the result of the pre-study and the survey a prototype was planned and implemented.</p><p>From the pre-study it was clear that the current solutions both took too long and used too much memory to be possible to use on a large social network. A number of algorithms were tested and from those a few was chosen to be implemented in the prototype. To help with the memory and time consumption the solution was also parallelized by using a partitioning algorithm to divide the graph into separate pieces on which each algorithm could run locally.The partitioning algorithm failed to scale well due to an internal modification of the partitioning scheme to adapt the partitioning to social graphs and simplify the parallelization. All but one algorithm scaled well and they were considerably faster than the original algorithms.</p>
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Grant, Eli. "Network analysis for social programme evaluation." Thesis, University of Oxford, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719991.
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Santoro, Lauren Ratliff. "Choosing to be Changed: How Selection Conditions the Effect of Social Networks on Political Attitudes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1498740182855649.
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FICARA, Annamaria. "Social network analysis approaches to study crime." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/537005.
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Social Network Analysis (SNA) studies groups of individuals and can be applied in a lot of areas such us organizational studies, psychology, economics, information science and criminology. One of the most important results of SNA has been the definition of a set of centrality measures (e.g., degree, closeness, betweenness, or clustering coefficient) which can be used to identify the most influential people with respect to their network of relationships.
The main problem with computing centrality metrics on social networks is the typical big size of the data. From the computational point of view, SNA represents social networks as graphs composed of a set of nodes connected by another set of edges on which the metrics of interest are computed.
To overcome the problem of big data, some computationally-light alternatives to the standard measures, such as Game of Thieves or WERW-Kpath, can be studied. In this regard, one of my main research activities was to analyze the correlation among standard and nonstandard centrality measures on network models and real-world networks.
The centrality metrics can greatly contribute to intelligence and criminal investigations allowing to identify, within a covert network, the most central members in terms of connections or information flow. Covert networks are terrorist or criminal networks which are built from the criminal relationships among members of criminal organizations. One of the most renowned criminal organizations is the Sicilian Mafia.
The focal point of my research work was the creation of two real-world criminal networks from the judicial documents of an anti-mafia operation called Montagna conducted by a specialized anti-mafia police unit of the Italian Carabinieri in Messina (i.e., the third largest city on the island of Sicily). One network includes meetings and the other one records telephone calls among suspected criminals of two Sicilian Mafia families. This dataset is unique and it might represent a valuable resource for better understanding complex criminal phenomena from a quantitative standpoint.
Different SNA approaches have been used on these Montagna networks to describe their structure and functioning, to predict missing links, to identify leaders or to evaluate police interventions aimed at dismantling and disrupting the networks. Graph distances have been used to find a network model able to properly mime the structure of a Mafia network and to quantify the impact of incomplete data not only on Mafia networks such as the Montagna ones but also on terrorist and street gangs networks.
The two simple Montagna networks have been finally used to build a multilayer network trying to obtain a more nuanced understanding of the network structure and of the strategic position of nodes in the network.
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Pister, Alexis. "Visual Analytics for Historical Social Networks : Traceability, Exploration, and Analysis." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG081.
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Cette thèse vise à identifier théoriquement et concrètement comment l'analyse visuelle peut aider les historiens dans leur processus d'analyse de réseaux sociaux. L'analyse de réseaux sociaux est une méthode utilisée en histoire sociale qui vise à étudier les relations sociales au sein de groupes d'acteurs (familles, institutions, entreprises, etc.) en reconstruisant les relations du passé à partir de documents historiques, tels que des actes de mariages, des actes de naissances, ou des recensements. L'utilisation de méthodes visuelles et analytiques leurs permet d'explorer la structure sociale formant ces groupes et de relier des mesures structurelles à des hypothèses sociologiques et des comportements individuels. Cependant, l'inspection, l'encodage et la modélisation des sources menant à un réseau finalisé donnent souvent lieu à des erreurs, des distorsions et des problèmes de traçabilité, et les systèmes de visualisation actuels présentent souvent des défauts d'utilisabilité et d'interprétabilité. En conséquence, les historiens ne sont pas toujours en mesure de faire des conclusions approfondies à partir de ces systèmes : beaucoup d'études se limitent à une description qualitative d'images de réseaux, surlignant la présence de motifs d'intérêts (cliques, îlots, ponts, etc.). Le but de cette thèse est donc de proposer des outils d'analyse visuelle adaptés aux historiens afin de leur permettre une meilleur intégration de leur processus global et des capacités d'analyse guidées. En collaboration avec des historiens, je formalise le processus d'une analyse de réseau historique, de l'acquisition des sources jusqu'à l'analyse finale, en posant comme critère que les outils utilisés dans ce processus devraient satisfaire des principes de traçabilité, de simplicité et de réalité documentaire (i.e., que les données présentées doivent être conformes aux sources) pour faciliter les va-et-vient entre les différentes étapes et la prise en main par l'utilisateur et ne pas distordre le contenu des sources. Pour satisfaire ces propriétés, je propose de modéliser les sources historiques en réseaux sociaux bipartis multivariés dynamiques avec rôles. Ce modèle intègre explicitement les documents historiques sous forme de nœuds, ce qui permet aux utilisateurs d'encoder, de corriger et d'analyser leurs données avec les mêmes outils. Je propose ensuite deux interfaces d'analyse visuelle permettant, avec une bonne utilisabilité et interprétabilité, de manipuler, d'explorer et d'analyser ce modèle de données. Le premier système ComBiNet offre une exploration visuelle de l'ensemble des dimensions du réseau à l'aide de vues coordonnées et d'un système de requêtes visuelles permettant d'isoler des individus ou des groupes et de comparer leurs structures topologiques et leurs propriétés. L'outil permet également de détecter les motifs inhabituels et ainsi de déceler les éventuelles erreurs dans les annotations. Le second système, PK-Clustering, est une proposition d'amélioration de l'utilisabilité et de l'efficacité des mécanismes de clustering dans les systèmes de visualisation de réseaux sociaux. L'interface permet de créer des regroupements pertinents à partir des connaissances a priori de l'utilisateur, du consensus algorithmique et de l'exploration du réseau dans un cadre d'initiative mixte. Les deux systèmes ont été conçus à partir des besoins et retours continus d'historiens, et visent à augmenter la traçabilité, la simplicité, et la réalité documentaire des sources dans le processus d'analyse de réseaux historiques. Je conclus sur la nécessité d'une meilleure intégration des systèmes d'analyse visuelle dans le processus de recherche des historiens. Cette intégration nécessite des outils plaçant les utilisateurs au centre du processus avec un accent sur la flexibilité et l'utilisabilité, limitant ainsi l'introduction de biais et les barrières d'utilisation des méthodes quantitatives, qui subsistent en histoire<br>This thesis aims at identifying theoretically and concretely how visual analytics can support historians in their social network analysis process. Historical social network analysis is a method to study social relationships between groups of actors (families, institutions, companies, etc.) through a reconstruction of relationships of the past from historical documents, such as marriage acts, migration forms, birth certificates, and censuses. The use of visualization and analytical methods lets social historians explore and describe the social structure shaping those groups while explaining sociological phenomena and individual behaviors through computed network measures. However, the inspection and encoding of the sources leading to a finalized network is intricate and often results in inconsistencies, errors, distortions, and traceability problems, and current visualization tools typically have usability and interpretability issues. For these reasons, social historians are not always able to make thorough historical conclusions: many studies consist of qualitative descriptions of network drawings highlighting the presence of motifs such as cliques, components, bridges, etc. The goal of this thesis is therefore to propose visual analytics tools integrated into the global social historians' workflow, with guided and easy-to-use analysis capabilities. From collaborations with historians, I formalize the workflow of historical network analysis starting at the acquisition of sources to the final visual analysis. By highlighting recurring pitfalls, I point out that tools supporting this process should satisfy traceability, simplicity, and document reality principles to ease bask and forth between the different steps, provide tools easy to manipulate, and not distort the content of sources with modifications and simplifications. To satisfy those properties, I propose to model historical sources into bipartite multivariate dynamic social networks with roles as they provide a good tradeoff of simplicity and expressiveness while modeling explicitly the documents, hence letting users encode, correct, and analyze their data with the same abstraction and tools. I then propose two interactive visual interfaces to manipulate, explore, and analyze this data model, with a focus on usability and interpretability. The first system ComBiNet allows an interactive exploration leveraging the structure, time, localization, and attributes of the data model with the help of coordinated views and a visual query system allowing users to isolate interesting groups and individuals, and compare their position, structures, and properties. It also lets them highlight erroneous and inconsistent annotations directly in the interface. The second system, PK-Clustering, is a concrete proposition to enhance the usability and effectiveness of clustering mechanisms in social network visual analytics systems. It consists in a mixed-initiative clustering interface that let social scientists create meaningful clusters with the help of their prior knowledge, algorithmic consensus, and interactive exploration of the network. Both systems have been designed with continuous feedback from social historians, and aim to increase the traceability, simplicity, and document reality of visual analytics supported historical social network research. I conclude with discussions on the potential merging of both tools, and more globally on research directions towards better integration of visual analytics systems on the whole workflow of social historians. Systems with a focus on those properties---traceability, simplicity, and document reality---can limit the introduction of bias while lowering the requirements for the use of quantitative methods for historians and social scientists which has always been a controversial discussion among practitioners
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Kulkarni, Rohan A. "Coolhunting and Coolfarming : harnessing the power of collaborative innovation networks using social network analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90713.
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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-73).<br>Email, Instant Messaging, Voice Over IP (VOIP) and other means of online communication have become so ubiquitous today that we rarely take a moment to acknowledge how the internet has changed and redefined the ways in which we communicate and collaborate with fellow human beings. The internet has empowered us to collaborate with others in ways that were not possible till just a few years ago. As we communicate and interact with each other and form relationships, we weave intricate Social Networks that can be analyzed and exhibit communication patterns that can be quantified. In this thesis I have applied Social Network Analysis based techniques that constitute Coolhunting (Gloor & Cooper, 2007) to analyze E-Mail and WebEx communications of sales professionals of a large technology company. I have quantified communication patterns and computed metrics of social network prominence such as degree and betweenness centralities using Condor, a Social Network Analysis and Coolhunting software. Several significant correlations between the success of sales professionals and these quantified communication patterns and centrality measures were found. The communication patterns and centralities of the sales professionals exhibited several traits of Collaborative Innovation Networks or COINs (Gloor, 2006). I have assessed the implications of these communication patterns and correlations and applied the concept of Coolfarming (Gloor, 2011 a) to make recommendations to the technology company on how it could leverage the power of these COINs to their advantage. Key Terms: Collaborative Innovation Networks (COINs), Coolhunting, Coolfarming, Social Network Analysis, Condor, E-Mail, WebEx<br>by Rohan Kulkarni.<br>S.M. in Engineering and Management
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Marra, Marianna. "Indirect ties in knowledge networks : a social network analysis with ordered weighted averaging operators." Thesis, Aston University, 2015. http://publications.aston.ac.uk/25303/.
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This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.
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Davel, Ronél. "Enriching knowledge networks - considering synergies between social network analysis, communities of practice and knowledge." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61289.
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The constructive management of existing knowledge and the access to and
development of new knowledge has become essential to organisations. Since tacit
knowledge can often not be captured or documented, knowledge is often created and
shared through social interaction within organisations. Relationships are thus
fundamental to knowledge creation and knowledge transfer and the various forms of
social networks existing within organisations play a primary role in leveraging these
relationships. This study followed the socialisation philosophy as reflected in the
works of Nonaka and Takeuchi (1995) and Hansen et al. (1999), where the creation
and sharing of knowledge occurs primarily by way of social interaction between
individuals. The said interaction typically occurs within informal networks, also
known as knowledge networks (Helms & Buijsrogge 2006).
In recent times there has been a growing awareness of social network analysis (SNA)
as an instrument to plot knowledge and expertise as well as to confirm the character
of connections in informal networks (Cross et al. 2004; Chan & Liebowitz 2006;
Müller-Prothmann 2006; Murale & Raju 2013; Cooke & Hall 2013; D'Errico et al.
2014). In line with the aforementioned studies, this study intended to investigate how
the integration of networking into KM can produce significant advantages for
organisations.
This research intended to outline a method for organisations to strengthen their social
capital by analysing, shaping and reinforcing their knowledge networks, thereby
enhancing the manner in which they share and create knowledge. Subsequently the
main research problem of this study was to investigate how knowledge networks can
be improved as a result of synergies between SNA, CoPs and knowledge maps. The
researcher attempted to illustrate via this question that cultivating synergies between
SNA, CoPs and knowledge maps will enable organisations to produce stronger
knowledge networks and ultimately increase their social capital.
In order to execute this study, the researcher developed a process map with the aim of
demonstrating exactly how knowledge networks could be advanced as a result of
synergies between SNA, CoPs and knowledge maps. This process map - which
answers the "how" in this question - is presented as the new contribution that this
study makes towards any organisation wanting to reinforce knowledge networks. It is
anticipated that this research will enable organisations to enrich their knowledge networks and expand their social capital by building on the process map that was
developed and implemented in this study.<br>Thesis (PhD)--University of Pretoria, 2017.<br>Information Science<br>PhD<br>Unrestricted
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Fares, Julian. "Modelling Stakeholder Integration Using Social Networks: An Australian Integrated Health Care Project." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20455.
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Stakeholders form relationships in projects to achieve both personal and project objectives. Proper stakeholder identification, categorisation and engagement methods that capture the social processes of the stakeholder network environment are lacking in many project management standards. In this study, social network theories and analytics are introduced as a new lens for stakeholder analysis to examine an integrated network of health care stakeholders (health care services and providers) that provides care for patients. The aim is to identify influential key stakeholders and determine the optimal network structure and composition for stakeholder integration (integrated care). A quantitative, whole network study was conducted where 56 health care providers were asked to report on their network relationships and the extent to which services are integrated in a geographic region in NSW, Australia. The results show that social network structure, position and relation constructs have a vital role in integrating health care stakeholders. More precisely, it was shown that ego-density, degree and betweenness centrality, tie strength and functional diversity have a positive association with service integration. In contrast, network efficiency, constraint and reciprocated relationships were found to be negatively associated with service integration. The research implications for the project management community are that stakeholders can be analysed and managed according to their relational attributes. With respect to integrated care, all stakeholders involved in integrated care projects should consider relationships configurations in their integration endeavour. Social network analysis is shown to be a vital tool for evaluating service integration where it identifies which services are currently working together; which ones are not working with others; where are the gaps in the relationships that can be strengthened and addressed.
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Rosen, Joel Louis. "Friends with benefits : an investigation into the social dynamics of network creation in the born-global SME." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/22826.
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Previous literature on the social dynamics of network creation in enterprises has drawn a sharp division between the utility of personal and professional networks. This has been particularly marked in social network analysis of born-global SMEs operating in emerging markets and seeking to internationalise. Using the case study of International Housing Solutions (Pty) Ltd (IHS) – a born global SME with both a global and a regional network – this research creates a deeper and more nuanced understanding of what such networks look like, what human factors are key to their operation, and what the relative importance is of the personal and professional drivers of networking.The study employs a mixed-method research design including network mapping and both qualitative and quantitative analysis of questionnaire responses from 35 participants in the IHS network, providing both hard data and rich qualitative insights into the ingredients and processes required for effective networking in such an enterprise.The results provide robust evidence for crossover between professional and personal networking activities; both are equally relevant in enabling the born-global SME to grow networks, increase innovation and enter otherwise impenetrable markets. Though the weighting of networking attributes is marginally different – for personal networks, the key attributes are advice, trust, friendship and communication; for professional networks, knowledge and referrals – in practice, both the personal and the professional are assimilated into a single complex of network activity and cannot be viewed in isolation.The research thus contributes innovative findings to a hitherto under-researched aspect of networking in the born-global SME.<br>Dissertation (MBA)--University of Pretoria, 2012.<br>Gordon Institute of Business Science (GIBS)<br>unrestricted
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Kibanov, Mark [Verfasser]. "Social Network Mining for Analysis of Social Phenomena / Mark Kibanov." Kassel : Universitätsbibliothek Kassel, 2019. http://d-nb.info/1193090261/34.
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Rajasekaran, Sathya Dev Squicciarini Anna C. Metzner John J. "Social network risk analysis and privacy framework." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4812/index.html.
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Afrasiabi, Rad Amir. "Social Network Analysis and Time Varying Graphs." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34441.
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The thesis focuses on the social web and on the analysis of social networks with particular emphasis on their temporal aspects. Social networks are represented here by Time Varying Graphs (TVG), a general model for dynamic graphs borrowed from distributed computing.
In the first part of the thesis we focus on the temporal aspects of social networks. We develop various temporal centrality measures for TVGs including betweenness, closeness, and eigenvector centralities, which are well known in the context of static graphs. Unfortunately the computational complexities of these temporal centrality metrics are not comparable with their static counterparts. For example, the computation of betweenness becomes intractable in the dynamic setting. For this reason, approximation techniques will also be considered. We apply these temporal measures to two very different datasets, one in the context of knowledge mobilization in a small community of university researchers, the other in the context of Facebook commenting activities among a large number of web users. In both settings, we perform a temporal analysis so to understand the importance of the temporal factors in the dynamics of those networks and to detect nodes that act as “accelerators”.
In the second part of the thesis, we focus on a more standard static graph representation. We conduct a propagation study on YouTube datasets to understand and compare the propagation dynamics of two different types of users: subscribers and friends. Finally, we conclude the thesis with the proposal of a general framework to present, in a comprehensive model, the influence of the social web on e-commerce decision making.
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Rezaee, Shaliz. "E-mail Prioritization through Social Network Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3356.
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Context. Trust and reliability are important issues in online communication. By rapid growth of online social networks (OSNs), online communication becomes richer by the integrating of social interaction into the communication model. However, E-mail communication systems concern about unsolicited messages. Objectives. In this thesis the aim is to investigate how to prioritize E-mails between recipients and senders by using information from OSNs. Methods. An algorithm is presented for computing trust by measuring users‟ interaction and similarity in online social networks and this trust is used by another algorithm for prioritizing the E-mail inbox. Results. An evaluation of the proposed method is performed via a case study and the prediction error of the method is compared with the prediction error of the random feedback. The error of the method is significantly lower than random feedback and is relatively low, given the small number of observations. Conclusions. This thesis contributes in its review and categorization of existing trust models. Furthermore, it provides an analysis on how to use social information for E-mail prioritization. Based on the analysis, a method is presented for improving the reliability of E-mail communication by extracting information from OSNs. The information is used for computing the trust score between two OSN friends. In this thesis, it is suggested that, inbox prioritization is achievable using the selected method.<br>This thesis has addressed E-mail prioritization through social network by using social information. The task has been done by focusing on the interaction and similarity between friends in the OSN. A theoretical analysis has been performed in order to identify the characteristic of suitable trust model. An algorithm (Algorithm 1) has been suggested to estimate weights of different criteria of social information. In order to have the trust predictions based on the user‟s preferences, the algorithm adjusted the weights based on the user‟s feedback. In addition, another algorithm (Algorithm 2) has been proposed to compute trust scores and prioritize E-mails inbox. Finally, an algorithm (Algorithm 3) has been presented to evaluate the error of the computed (predicted) trust scores. In order to display the applicability of the method as well as to motivate the theoretical foundation, a case study was reported in which the proposed method was applied to Facebook. The analysis showed that the proposed method was feasible to be used, and it provided users a mean to prioritize E-mail inboxes based on the social information extracted from Facebook. The analysis indicated that least squares method was a suitable approach to estimate weights that were used in computing trust scores and thus prioritizing E-mails inbox.
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Wang, Xin. "Graph pattern matching on social network analysis." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8277.
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Graph pattern matching is fundamental to social network analysis. Its effectiveness for identifying social communities and social positions, making recommendations and so on has been repeatedly demonstrated. However, the social network analysis raises new challenges to graph pattern matching. As real-life social graphs are typically large, it is often prohibitively expensive to conduct graph pattern matching over such large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded simulation, and quadratic time for simulation. These hinder the applicability of graph pattern matching on social network analysis. In response to these challenges, the thesis presents a series of effective techniques for querying large, dynamic, and distributively stored social networks. First of all, we propose a notion of query preserving graph compression, to compress large social graphs relative to a class Q of queries. We then develop both batch and incremental compression strategies for two commonly used pattern queries. Via both theoretical analysis and experimental studies, we show that (1) using compressed graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr as well as its maintenance can be processed efficiently. Secondly, we investigate the distributed graph pattern matching problem, and explore parallel computation for graph pattern matching. We show that our techniques possess following performance guarantees: (1) each site is visited only once; (2) the total network traffic is independent of the size of G; and (3) the response time is decided by the size of largest fragment of G rather than the size of entire G. Furthermore, we show how these distributed algorithms can be implemented in the MapReduce framework. Thirdly, we study the problem of answering graph pattern matching using views since view based techniques have proven an effective technique for speeding up query evaluation. We propose a notion of pattern containment to characterise graph pattern matching using views, and introduce efficient algorithms to answer graph pattern matching using views. Moreover, we identify three problems related to graph pattern containment, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Fourthly, we revise graph pattern matching by supporting a designated output node, which we treat as “query focus”. We then introduce algorithms for computing the top-k relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively, with early termination property. Furthermore, we investigate the diversified top-k matching problem, and develop an approximation algorithm with performance guarantee and a heuristic algorithm with early termination property. Finally, we introduce an expert search system, called ExpFinder, for large and dynamic social networks. ExpFinder identifies top-k experts in social networks by graph pattern matching, and copes with the sheer size of real-life social networks by integrating incremental graph pattern matching, query preserving compression and top-k matching computation. In particular, we also introduce bounded (resp. unbounded) incremental algorithms to maintain the weighted landmark vectors which are used for incremental maintenance for cached results.
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Bohn, Angela, Norbert Walchhofer, Patrick Mair, and Kurt Hornik. "Social Network Analysis of Weighted Telecommunications Graphs." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/708/1/document.pdf.
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SNA provides a wide range of tools that allow examination of telecommunications graphs. Those graphs contain vertices representing cell phone users and lines standing for established connections. Many sna tools do not incorporate the intensity of interaction. This may lead to wrong conclusions because the difference between best friends and random contacts can be defined by the accumulated duration of talks. To solve this problem, we propose a closeness centrality measure (ewc) that incorporates line values and compare it to Freeman's closeness. Small exemplary networks will demonstrate the characteristics of the weighted closeness compared to other centrality measures. Finally, the ewc will be tested on a real-world telecommunications graph provided by a large Austrian mobile service provider and the advantages of the ewc will be discussed.<br>Series: Research Report Series / Department of Statistics and Mathematics
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Nohuddin, Puteri. "Predictive trend mining for social network analysis." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7153/.
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This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established.
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Hu, Daning. "Analysis and Applications of Social Network Formation." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/145710.
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Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.
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MACCAGNOLA, DANIELE. "Relational Learning Models for Social Network Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/100459.
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Social networks have been studied for nearly half a century by sociologists to analyze interactions between people. Nowadays, with the advent of Web 2.0, social networks have moved from being an abstract concept to actual online applications such as Facebook, Twitter and Linkedin, which are used daily by people to create and maintain relationships with friends, co-workers and other acquaintances.
However, online social networks allow their users to do more than just maintain friendships: people can generally create and share content in various forms, from simple textual messages (called posts) to photos, videos, audios and much more.
Several approaches in Social Network Analysis have been proposed in the years to extract knowledge from social networks, addressing tasks that ranges from understanding how users create and modify their relationships, to finding the most influential people in a group, to understanding the ideas and opinions expressed by people in their posts.
Many techniques from the field of Machine Learning have been used to address these problems. While some of them exploit the relationships among users, others focus on the content generated by the users, typically by analyzing the textual content written in the posts. These approaches, however, are generally unable to exploit both, in this way ignoring a consistent part of information available in social networks.
The field of Relational Learning tries to overcome this limitation, by extending traditional approaches in order to use both sources of information, and thus achieve better performances.
In this thesis, I propose new Relational Learning approaches that address two tasks in Social Network Analysis. The first task is Community Discovery, which objective is to detect groups of users that share strong connections (e.g. working in the same company, attended the same school, etc.) or sharing the same interests. While this task is generally addressed by considering only the network structure, adding the user content can allow to increase the performance.
The second task is Opinion Detection, which objective is to infer the opinion of users about a specific topic (politics, likeness of a brand). This task is typically addressed using user textual content, but the relationships can provide additional insights that allow to improve the inference of users' opinions.
The experimental investigations reveal that network structure and user-generated content provide complementary information, and that using both sources of data can improve the performance of algorithms in both community discovery (a structure-based task) and opinion detection (a content-based task).
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Hultin, Alex. "Sustaining interdisciplinary research : a multilayer perspective." Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767571.
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Interdisciplinary Research (IDR) has received a lot of attention from academics, policy-makers, and decision-makers alike. RCUK invests £3 billion in research grants each year (RCUK 2017); half of the grants are provided to investigators who hail from different departments. There is mounting awareness of the challenges facing IDR, and a large body of literature trying to establish how IDR can be analysed (Davidson 2015, Yegros-Yegros, Rafols et al. 2015). Of these, the majority have been qualitative studies and it has been noticed that there is a distinct lack of quantitative studies that can be used to identify how to enable IDR. The literature shows that many of the barriers to IDR can be classified as either cultural or administrative (Katz and Martin 1997, Cummings and Kiesler 2005, Rafols 2007, Wagner, Roessner et al. 2011), neither of which are easily changed over a short period of time. The perspective taken in this research is that change can be affected by enabling the individuals who conduct IDR. Herein lies the main challenge; how can these future leaders of IDR be identified so that they can be properly supported. No existing datasets were deemed suitable for the purpose, and a new dataset was created to analyse IDR. To isolate dynamics within an organisation, hard boundaries were drawn around research-organisations. The University of Bath journal co-authorship dataset 2000-2017 was determined to be suitable for this purpose. From this dataset a co-authorship network was created. To analyse this, established models from literature were adapted and used to identify differences in disciplinary and interdisciplinary archetypes. This was done through a correlational study. No statistically significant differences between such author archetypes were found. It was therefore concluded that an alternative approach was necessary. By adapting the networks framework to account for different types of links between edges, a multilayer perspective was adopted. This resulted in a rank-3 tensor, node-aligned framework being proposed, allowing disciplines to be represented in the network. By using this framework to construct the University of Bath multiplex co-authorship network, an exemplar structure was established through use of a series of proposed structural metrics. A growth model was proposed and successfully recreated the structure and thereby uncovered mechanics affecting real-world multiplex networks. This highlighted the importance of node entities and the layer closeness centrality. This implies that it is very difficult to carry over benefits across disciplines, and that some disciplines are better suited to share and adapt knowledge than others. The growth model also allowed an analytical expression for the rate of change of disciplinary degree, thereby providing a model for who is most likely to enable and sustain IDR.
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Zhang, Huiqi. "Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30533/.
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The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new reciprocity index to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution.
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Molin, Sigrid. "Social nätverksanalys som ett redskap vid brottsutredningar." Thesis, Malmö högskola, Fakulteten för hälsa och samhälle (HS), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-25489.
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Genom en systematisk litteraturöversikt i kombination med en intervju är syftet med denna uppsats att försöka beskriva varför den sociala nätverksanalysen är lämplig i brottsutredningssammanhang samt hur den sociala nätverksanalysen används i brottsutredningar. Tanken är också att översikten ska kunna bidra till att se vilka möjligheter det finns att praktiskt utveckla metoden. Det finns en hel del forskning kring både social nätverksanalys (SNA) som metod och som teori och det används idag inom en mängd olika områden. Som teori handlar SNA om hur vi människor är sociala varelser som påverkar varandra i de tankar vi har och i de val som vi gör. Som metod är SNA istället olika matematiska uträkningar som kan användas för att beskriva mänskliga relationer. Inom kriminologin är SNA relativt nytt trots att brott i sig ofta är ett ”nätverksfenomen”. Flera kriminologiska teorier trycker också på betydelsen av att den egna brottsligheten har ett samband med de personer som vi umgås med. Resultatet visar att det finns klara fördelar med att använda sig av SNA i en brottsutredning, strukturer och nyckelpersoner kan identifieras, något som inte alltid hade kunnat ske utan teknikens hjälp. Den data som i utredningssammanhang används till nätverksanalyser är vanligtvis kvantitativa data, exempelvis telefontrafik. Olika typer av data kan ge väldigt olika resultat och blir det fel i datainsamling kan det sabotera för hela analysen. Det behövs mer teoretisk forskning kring SNA för att den som metod ska kunna appliceras på kriminologisk teori och på sikt även kunna användas bättre i utredningssammanhang. Ett stort problem med att forska om metoden är att den kvantitativa datan kan vara svår att få tag på, det finns därför väldigt lite litteratur om hur social nätverksanalys kan användas i brottsutredningar.<br>With a systematic literature review and an interview, the aim of this essay is to try to describe how the social network analysis (SNA) is used in criminal investigations. Hopefully, the essay can also help in pointing out why future research is needed and in what direction that research should go. As a theory, SNA focuses on man as a social being and how we affect each other in the way we think and act. As a method SNA is a number of mathematical computations that aims to explain relationships. There is a large amount of research about social network analysis, both as a theory and as a method but in the criminological field SNA is still relatively new. That is surprising as many criminological theories focuses on the importance of the people we engage with and our own delinquency. The result in this essay shows that there are many advantages with using SNA in a criminal investigation, structures and key-persons becomes more visible which sometimes is hard without technology. Different types of data can generate very different results and if something goes wrong in the collection of data it can sabotage the entire analysis. There is a need for more theoretical research on SNA so that it, as a method, can be applied to criminological theory and later to criminal investigations. There is a big problem when doing research about social networks, the access to network-data. It is very hard to collect and is usually only available to police-officers or other qualified groups. Therefore the amount of literature in the subject is limited.
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Tandefelt, Max. "Web 2.0 and Network Society : -PR and Communication: The Challenge of Online Social Networks." Thesis, Uppsala University, Media and Communication, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9187.
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<p>Abstract</p><p>As online social network services are becoming one of the dominant media channels the importance of disseminating messages through them is of high importance for governments, organizations, companies etc. The online social network services are several and changes rapidly as they grow and evolve. Being networks, the services give the user the tools to send, as well as receive text and information. This proposes us with yet another obstacle in communication via online social network services since sender and receiver merges together.</p><p>Online social network services and the Blogosphere, which essentially also is a network, exist in the context of Web 2.0. The crucial feature of Web 2.0 is to a large degree the harnessing of collective intelligence i.e. the collection of individual knowledge and information. Many of the tools and sites within Web 2.0 are therefore of a network structure, hence further stressing the importance to communicate via networks in general.</p><p>Network Analysis is the discipline through which we can see and understand the larger patterns of networks. In this thesis I have looked into three key concepts of Network Analysis; Weak Links, Growth and Preferential Attachment. I have found that we can use the knowledge of Network Analysis to disseminate messages via online social network services since it provides us with the raw structures of how networks tend to grow, and how messages tend to disseminate.</p><p>Title: Web 2.0 and Network Society – PR and Communication: The Challenge of Online Social Networks</p><p>Number of pages: 34</p><p>Author: Max Tandefelt</p><p>Tutor: Else Nygren</p><p>Course: Media and Communication Studies C</p><p>Period: HT 07</p><p>University: Division of Media and Communication, Department of Information Science, Uppsala University.</p><p>Purpose/Aim: Facilitate message dissemination through online social network services, as they are becoming one of the dominant media channels</p><p>Material/Method: Network Analysis</p><p>Main results: I have presented crucial concepts of Network Analysis that can be used for message dissemination via online social network services</p><p>Keywords: Online Social Network Services, Network Analysis, Web 2.0, Message Dissemination</p>
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Lo, Giudice Valeria. "Capitale sociale e partenariato locale: un applicazione della Social Network Analysis nella Provincia di Siracusa." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1064.
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Gli studi della sociologia classica inquadrano Bourdieu e Coleman da una parte e Putnam dall altra come i promotori di due importanti modelli di relazioni generatrici di capitale sociale, rispettivamente basati su un approccio di tipo individualistico e di tipo collettivistico.
Nonostante le due teorie abbiano dato spunto a linee di ricerca sociologiche contrapposte trovano un punto comune nell opinione condivisa che il capitale sociale sia una risorsa fondata sull esistenza di un qualche tipo di relazione sociale (G. Degli Antoni, 2005).
Questo concetto è condiviso negli studi di analisi delle reti proposti da Mark Granovetter, che studia come gli aspetti sociali influenzano il funzionamento del sistema economico e, considera il capitale sociale come reti di rapporti interpersonali all interno dei quali circolano opportunità ed informazioni che la razionalità degli individui permetterebbe di utilizzare. Nell ottica dello sviluppo locale, tale teoria è in sintonia con le nuove politiche adottate sia a livello comunitario nazionale e regionale.
Sulla base di questi studi, nel presente lavoro di tesi, viene effettuato, tramite tecniche di social network analysis, uno studio delle dimamiche di sviluppo locale del Compresorio Val d Anapo che interessa parte della provincia di Siracusa, allo scopo di individuare strumenti e momenti chiave di partenariato locale come forma di governance territoriale.
La programmazione di questo Comprensorio si è manifestata operativamente attraverso esperienze interessanti che lo hanno visto protagonista nella definizione ed attuazione dei processi di crescita locali.
I risultati forniscono una prima mappa delle relazioni esistenti sul territorio, che fornisce importanti indicazioni per i futuri processi di pianificazione integrata unitamente alle analisi relative alle caratteristiche del tessuto economico-sociale dell area.
L analisi ha evidenziato la capacità e l interesse degli attori locali di organizzarsi in forme di Partenariato socio-economico che, rappresentando gli interessi economici e sociali dell area di riferimento, hanno svolto un ruolo chiave nella fase di programmazione delle risorse.
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Lam, Phuongthao Tuyen. "Examining Sexually Transmitted Disease Transmission Dynamics in Chlamydia Positive and Negative Adolescent Population using Social Network Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/iph_theses/78.
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Adolescents are disproportionately affected by a wide range of STDs due to high level of personal risk behaviors and poor access to STD prevention services. As documented in numerous previous studies, STDs could lead to many serious consequences to adolescents’ health and the overall well being of society. One prominent concern is that STDs increase adolescent’s risk in acquiring HIV infection. Among all STDs, Chlamydia is the most prevalent in adolescents as well as in the general population. No previous studies have attempted to examine the social interaction of adolescent population heavily affected by Chlamydia. In this study, we would like to take a step forward to identify the difference in behavioral risk level between Chlamydia positive and negative adolescent social network and to describe any impacts of these groups on the transmission of other STDs using social network analysis of data collected from adolescent population in Dekalb County, Georgia. The results indicated highest behavioral risk in the negative girl index respondents’ contacts followed by those of positive boys, positive girls and finally negative boys. However STD prevalence in the contacts among these different groups did not follow the same pattern. Prevalence of STD is highest in the negative girls’ contact group followed by that of positive boys, negative boys; and interestingly positive boys’ contacts exhibit the lowest STD rate. As informed by the results, the presence of infection is not a sufficient indicator of risks; thus, network characteristic was also examined to accurately determine transmission dynamics in this population. Social and sexual network structures among these four different index groups and their contacts suggested low level of STD transmission.
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Nordvik, Monica K. "Contagious Interactions : Essays on social and epidemiological networks." Doctoral thesis, Stockholm : Visby : Acta Universitatis Stockholmiensis ; eddy.se [distributör], 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8309.
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McGlohon, Mary. "Structural Analysis of Large Networks: Observations and Applications." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/18.
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Network data (also referred to as relational data, social network data, real graph data) has become ubiquitous, and understanding patterns in this data has become an important research problem. We investigate how interactions in social networks are formed and how these interactions facilitate diffusion, model these behaviors, and apply these findings to real-world problems.
We examined graphs of size up to 16 million nodes, across many domains from academic citation networks, to campaign contributions and actor-movie networks. We also performed several case studies in online social networks such as blogs and message board communities.
Our major contributions are the following: (a) We discover several surprising patterns in network topology and interactions, such as Popularity Decay power law (in-links to a blog post decay with a power law with -1:5 exponent) and the oscillating size of connected components; (b) We propose generators such as the Butterfly generator that reproduce both established and new properties found in real networks; (c) several case studies, including a proposed method of detecting misstatements in accounting data, where using network effects gave a significant boost in detection accuracy.
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Wei, Wei. "Utilizing Social Bookmarking Tag Space for Web Content Discovery: A Social Network Analysis Approach." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195123.
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Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based on tags, as well as following the user-user connections formed in the social bookmarking community. However, the effectiveness of tag-based search is limited due to the lack of explicitly represented semantics in the tag space. In addition, social connections between users are underused for web content discovery because of the inadequate social functions. In this research, we propose a comprehensive framework to reorganize the flat tag space into a hierarchical faceted model. We also studied the structure and properties of various networks emerging from the tag space for the purpose of more efficient web content discovery.The major research approach used in this research is social network analysis (SNA), together with methodologies employed in design science research. The contribution of our research includes: (i) a faceted model to categorize social bookmarking tags; (ii) a relationship ontology to represent the semantics of relationships between tags; (iii) heuristics to reorganize the flat tag space into a hierarchical faceted model using analysis of tag-tag co-occurrence networks; (iv) an implemented prototype system as proof-of-concept to validate the feasibility of the reorganization approach; (v) a set of evaluations of the social functions of the current networking features of social bookmarking and a series of recommendations as to how to improve the social functions to facilitate web content discovery.
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Friggeri, Adrien. "A Quantitative Theory of Social Cohesion." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00737199.
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Community, a notion transversal to all areas of Social Network Analysis, has drawn tremendous amount of attention across the sciences in the past decades. Numerous attempts to characterize both the sociological embodiment of the concept as well as its observable structural manifestation in the social network have to this date only converged in spirit. No formal consensus has been reached on the quantifiable aspects of community, despite it being deeply linked to topological and dynamic aspects of the underlying social network. Presenting a fresh approach to the evaluation of communities, this thesis introduces and builds upon the cohesion, a novel metric which captures the intrinsic quality, as a community, of a set of nodes in a network. The cohesion, defined in terms of social triads, was found to be highly correlated to the subjective perception of communitiness through the use of a large-scale online experiment in which users were able to compute and rate the quality of their social groups on Facebook. Adequately reflecting the complexity of social interactions, the problem of finding a maximally cohesive group inside a given social network is shown to be NP-hard. Using a heuristic approximation algorithm, applications of the cohesion to broadly different use cases are highlighted, ranging from its application to network visualization, to the study of the evolution of agreement groups in the United States Senate, to the understanding of the intertwinement between subjects' psychological traits and the cohesive structures in their social neighborhood. The use of the cohesion proves invaluable in that it offers non-trivial insights on the network structure and its relation to the associated semantic.
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Bell, Patrick M. "Development of Local Homeland Security Networks in the State of Florida: A Social Network Analysis Approach." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/574.
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How do local homeland security organizations respond to catastrophic events such as hurricanes and acts of terrorism? Among the most important aspects of this response are these organizations ability to adapt to the uncertain nature of these “focusing events” (Birkland 1997). They are often behind the curve, seeing response as a linear process, when in fact it is a complex, multifaceted process that requires understanding the interactions between the fiscal pressures facing local governments, the institutional pressures of working within a new regulatory framework and the political pressures of bringing together different levels of government with different perspectives and agendas.
This dissertation has focused on tracing the factors affecting the individuals and institutions planning, preparing, responding and recovering from natural and man-made disasters. Using social network analysis, my study analyzes the interactions between the individuals and institutions that respond to these “focusing events.” In practice, it is the combination of budgetary, institutional, and political pressures or constraints interacting with each other which resembles a Complex Adaptive System (CAS).
To investigate this system, my study evaluates the evolution of two separate sets of organizations composed of first responders (Fire Chiefs, Emergency Management Coordinators) and community volunteers organized in the state of Florida over the last fifteen years. Using a social network analysis approach, my dissertation analyzes the interactions between Citizen Corps Councils (CCCs) and Community Emergency Response Teams (CERTs) in the state of Florida from 1996- 2011. It is the pattern of interconnections that occur over time that are the focus of this study.
The social network analysis revealed an increase in the amount and density of connections between these organizations over the last fifteen years. The analysis also exposed the underlying patterns in these connections; that as the networks became more complex they also became more decentralized though not in any uniform manner. The present study brings to light a story of how communities have adapted to the ever changing circumstances that are sine qua non of natural and man-made disasters
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Dang, The Anh. "Analysis of community in social networks." Paris 13, 2012. http://www.theses.fr/2012PA132043.
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Un réseau social est une structure composée d'entités reliées par un ou plusieurs types d'interdépendance, le plus souvent modélisé par un ou plusieurs graphes. Une caractéristique importante des réseaux sociaux est leur structure en communautés. Une communauté est définie comme un ensemble de nœuds qui interagissent d'avantage entre eux qu'avec le reste du réseau. Cette thèse porte sur l'analyse des communautés dans les réseaux sociaux, qui est utile pour de nombreuses tâches, telles la caractérisation de la structure, les systèmes de recommandation, la visualisation, ou encore le suivi de la dynamique. Nous proposons notamment des techniques pour découvrir les communautés dans les graphes bipartites, basé sur l'optimisation de modularités bipartites. Nous étudions ensuite la détection de communautés dans les graphes dont les nœuds sont associés à des attributs, comme cela est très souvent le cas dans les applications réelles. Nos algorithmes considèrent simultanément la structure et les attributs du graphe et détectent des communautés telles que les nœuds dans la même communauté soient densément connectés et portent des attributs proches. Les méthodes développées sont appliquées à l'analyse des communautés du site web social Skyrock et de réseaux de blogs, dans le cadre du projet ANR ExDEUSS CEDRES. Nous étudions aussi la contribution des informations extraites des communautés pour améliorer la performance des systèmes de recommandation. Enfin, nous proposons un modèle génératif de réseau social intégrant les attributs de nœuds et la structure des communautés, qui nous permet de proposer des jeux de tests artificiels simulant des réseaux complexes réels.
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Caulfield, John. "A social network analysis of Irish language use in social media." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/53228/.
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Statistics show that the world wide web is dominated by a few widely spoken languages. However, in quieter corners of the web, clusters of minority language speakers can be found interacting and sharing content. This study is the first to compare three such clusters of Irish language social media users. Social network analysis of the most active public sites of interaction through Irish – the Irish language blogosphere, the Irish language Twittersphere and a popular Irish language Facebook group – reveals unique networks of individuals communicating through Irish in unique and innovative ways. Firstly, it describes the members and their activity, and the size and structure of the networks they share. Then through focused discourse analysis of the core prolific users in each network it describes how the language has been adapted to computer-mediated communication. This study found that the largest networks of Irish speakers comprised between 150-300 regular participants each. Most members were adults, male, and lived in towns and cities outside of the language’s traditional heartland. Moreover, each group shared one common trait: though scattered geographically, through regular online interaction between core members they behave like communities. They were found to have shared histories, norms and customs, and self-awareness that their groups were unique. Furthermore, core users had adapted the language in new and innovative ways through their online discourse. This study is the first comprehensive audit of who is using the Irish language socially on the web, where they are forming networks online, and how they are adapting the language to online discourse. It makes a unique contribution in re-imagining what constitutes an Irish language community in the context of the Network Society. In the process, it contributes to the growing body of sociolinguistic research into globalisation and local identity on the web.
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Rezayidemne, Seyedsaed. "Characterizing Online Social Media: Topic Inference and Information Propagation." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23904.
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Word-of-mouth (WOM) communication is a well studied phenomenon in the literature and content propagation in Online Social Networks (OSNs) is one of the forms of WOM mechanism that have been prevalent in recent years specially with the widespread surge of online communities and online social networks. The basic piece of information in most OSNs is a post (e.g., a tweet in Twitter or a post in Facebook). A post can contain different types of content such as text, photo, video, etc, or a mixture of two or more them. There are also various ways to enrich the text by mentioning other users, using hashtags, and adding URLs to external contents. The goal of this study is to investigate what factors contribute into the propagation of messages in Google+. To answer to this question a multidimensional study will be conducted. On one hand this question could be viewed as a natural language processing problem where topic or sentiment of posts cause message dissemination. On the other hand the propagation can be effect of graph properties i.e., popularity of message originators (node degree) or activities of communities. Other aspects of this problem are time, external contents, and external events. All of these factors are studied carefully to find the most highly correlated attribute(s) in the propagation of posts.