Relevant bibliographies by topics / Social network analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Social network analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 December 2024

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Journal articles on the topic "Social network analysis"

1

Quinn, Darren, Liming Chen, and Maurice Mulvenna. "Social Network Analysis." International Journal of Ambient Computing and Intelligence 4, no. 3 (July 2012): 46–58. http://dx.doi.org/10.4018/jaci.2012070104.

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Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.
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Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Shcherbakov, V. S., and I. A. Karpov. "Regional Inflation Analysis Using Social Network Data." Economy of Regions 20, no. 3 (2024): 930–46. http://dx.doi.org/10.17059/ekon.reg.2024-3-21.

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Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by a range of factors, including inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. It is hypothesised that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of prices may also be in the focus of users’ discussions. So, such discussions could be regarded as an alternative source of more rapid information about inflation expectations. This study is based on unstructured data from VKontakte social network used to analyse upward and downward inflationary trends (on the example of the Omsk region). The sample of more than 8.5 million posts was collected between January 2010 and May 2022. The authors used BERT neural networks to solve the problem. These models demonstrated better results than the benchmarks (e.g., logistic regression, decision tree classifier, etc.). It makes possible to define pro-inflationary and disinflationary types of keywords in different contexts and get their visualisation with SHAP method. This analysis provides additional operational information about inflationary processes at the regional level The proposed approach can be scaled for other regions. At the same time, the limitation of the work is the time and power costs for the initial training of similar models for all regions of Russia.
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Rowley, Timothy J. "Social Network Analysis." Proceedings of the International Association for Business and Society 7 (1996): 999–1009. http://dx.doi.org/10.5840/iabsproc1996794.

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S., Sukharev O., and Kurmanov N.V. "Social Network Analysis." Advances in Economics and Business 2, no. 3 (March 2014): 121–26. http://dx.doi.org/10.13189/aeb.2014.020301.

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Jain, Susha, Mahaveer Jain, and Balasubramani R. "Social Network Analysis." IJARCCE 8, no. 5 (May 30, 2019): 236–40. http://dx.doi.org/10.17148/ijarcce.2019.8543.

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Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (July 2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.

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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.
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Scott, John. "Social Network Analysis." Sociology 22, no. 1 (February 1988): 109–27. http://dx.doi.org/10.1177/0038038588022001007.

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Streeter, Calvin L., and David F. Gillespie. "Social Network Analysis." Journal of Social Service Research 16, no. 1-2 (March 24, 1993): 201–22. http://dx.doi.org/10.1300/j079v16n01_10.

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Buštíková, Lenka. "Social Network Analysis." Czech Sociological Review 35, no. 2 (April 1, 1999): 193–206. http://dx.doi.org/10.13060/00380288.1999.35.2.10.

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Dissertations / Theses on the topic "Social network analysis"

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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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CURZI, MIRCO. "Content based social network analysis." Doctoral thesis, Università Politecnica delle Marche, 2009. http://hdl.handle.net/11566/242305.

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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.
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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
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]
IX n.s.
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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.
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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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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.

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.

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.

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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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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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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.
Doctor of Philosophy
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.
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10

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.

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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.
Doctor of Philosophy
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.
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Books on the topic "Social network analysis"

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Freeman, Linton. Social Network Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2008. http://dx.doi.org/10.4135/9781446263464.

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Knoke, David, and Song Yang. Social Network Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985864.

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Knoke, David. Social network analysis. 2nd ed. Los Angeles: Sage Publications, 2008.

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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Abraham, Ajith, Aboul-Ella Hassanien, and Vaclav Sná¿el, eds. Computational Social Network Analysis. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-229-0.

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Missaoui, Rokia, Talel Abdessalem, and Matthieu Latapy, eds. Trends in Social Network Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53420-6.

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Reis Pinheiro, Carlos Andre, ed. Social Network Analysis in Telecommunications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119200529.

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Pryke, Stephen. Social Network Analysis in Construction. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781118443132.

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Carrington, Peter. Applications of Social Network Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2014. http://dx.doi.org/10.4135/9781473915329.

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Book chapters on the topic "Social network analysis"

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Whitehead, James, and Mike Peckham. "Social Network Analysis." In Network Leadership, 105–19. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003092582-17.

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Medina, Richard, and Nigel Waters. "Social Network Analysis." In Handbook of Regional Science, 1–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-36203-3_49-1.

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Lee, Viktor. "Social network analysis." In How Firms Can Strategically Influence Open Source Communities, 111–26. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7140-1_6.

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Marcus, Sherry E., Melanie Moy, and Thayne Coffman. "Social Network Analysis." In Mining Graph Data, 443–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch17.

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Aggarwal, Charu C. "Social Network Analysis." In Data Mining, 619–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_19.

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Mukherjee, S. P., Bikas K. Sinha, and Asis Kumar Chattopadhyay. "Social Network Analysis." In Statistical Methods in Social Science Research, 135–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2146-7_13.

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Maya-Jariego, Isidro. "Social Network Analysis." In Encyclopedia of Quality of Life and Well-Being Research, 6134–35. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2777.

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Serrat, Olivier. "Social Network Analysis." In Knowledge Solutions, 39–43. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0983-9_9.

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Daming, Xu, Wang Xiaomei, and Li Wei. "Social Network Analysis." In The Blackwell Guide to Research Methods in Bilingualism and Multilingualism, 263–74. Oxford, UK: Blackwell Publishing Ltd., 2009. http://dx.doi.org/10.1002/9781444301120.ch15.

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Liu, Bing. "Social Network Analysis." In Web Data Mining, 269–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_7.

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Conference papers on the topic "Social network analysis"

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Lim, Hwee Ling. "Social network analysis." In the 2009 conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1551950.1551967.

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Maruyama, William Takahiro, and Luciano Antonio Digiampietri. "Co-authorship prediction in academic social network." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6445.

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The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.
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Nandi, Satrajit, Sucheta Chatterjee, Nilanjan Roy, and Raju Basak. "Societal Development Analysis based on Social Network." In 2019 International Conference on Computer, Electrical & Communication Engineering (ICCECE). IEEE, 2019. http://dx.doi.org/10.1109/iccece44727.2019.9001878.

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Scholand, Andrew J., Yla R. Tausczik, and James W. Pennebaker. "Social language network analysis." In the 2010 ACM conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1718918.1718925.

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Guo, Yuning, Jianxiang Cao, and Weiguo Lin. "Social Network Influence Analysis." In 2019 6th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2020. http://dx.doi.org/10.1109/dsa.2019.00093.

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Akhtar, Nadeem. "Social Network Analysis Tools." In 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.83.

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Frikken, Keith B., and Philippe Golle. "Private social network analysis." In the 5th ACM workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1179601.1179619.

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Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.

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In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.
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Ren, Fuxin, Zhongbao Zhang, Jiawei Zhang, Sen Su, Li Sun, Guozhen Zhu, and Congying Guo. "BANANA: when Behavior ANAlysis meets social Network Alignment." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/200.

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Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.
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Zhang, Yuan, and Changhang Wu. "Social network analysis based on social networks journal co-authorship." In 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), edited by M. A. Jabbar and Pascal Lorenz. SPIE, 2024. http://dx.doi.org/10.1117/12.3032912.

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Reports on the topic "Social network analysis"

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Bonnett, Michaela, Chimdi Ezeigwe, Meaghan Kennedy, and Teri Garstka. Using Social Network Analysis to Link Community Health and Network Strength. Orange Sparkle Ball, July 2023. http://dx.doi.org/10.61152/scsf6662.

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Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.
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Schmidt, Teresa. Statistical Analysis of Social Network Change. Portland State University Library, December 2019. http://dx.doi.org/10.15760/etd.7288.

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Johnson, Eric M., and Robert Chew. Social Network Analysis Methods for International Development. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.rb.0026.2105.

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Social Network Analysis (SNA) is a promising yet underutilized tool in the international development field. SNA entails collecting and analyzing data to characterize and visualize social networks, where nodes represent network members and edges connecting nodes represent relationships or exchanges among them. SNA can help both researchers and practitioners understand the social, political, and economic relational dynamics at the heart of international development programming. It can inform program design, monitoring, and evaluation to answer questions related to where people get information; with whom goods and services are exchanged; who people value, trust, or respect; who has power and influence and who is excluded; and how these dynamics change over time. This brief advances the case for use of SNA in international development, outlines general approaches, and discusses two recently conducted case studies that illustrate its potential. It concludes with recommendations for how to increase SNA use in international development.
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Evans, Daniel, Margaret Moten, Csilla Szabo, and Brian Macdonald. Social Network Analysis in Frontier Capital Markets. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada565112.

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Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. Latent Space Approaches to Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada458734.

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Bowman, Elizabeth K., Nkonko Kamwangamalu, Heather Roy, Alla Tovares, Sue Kase, Michelle Vanni, Mugizi R. Rwebangira, and Mohamed Chouikha. Exploring Social Meaning in Online Bilingual Text through Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada622463.

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Tanenbaum, William, and John Brand. Using AutoMap for Social and Texual Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada484740.

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Frantz, Terrill L., and Kathleen M. Carley. Treemaps as a Tool for Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada455979.

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Lipsky, Alyson, Molly Adams, and Chinyere Okeke. Ground-Truthing Social Network Analysis for Universal Health Coverage Advocacy Networks in Nigeria. RTI Press, May 2024. http://dx.doi.org/10.3768/rtipress.2024.pb.0028.2405.

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Achieving universal health coverage (UHC) requires commitment from a wide range of actors, including policy makers, civil society, and academics. In low- and middle-income countries, creating momentum among stakeholders can be challenging with competing priorities and limited funding. Advocacy coalitions—groups of like-minded organizations coalescing to achieve a common goal—have been used to achieve UHC; however, the effectiveness of advocacy coalitions for UHC is not well understood. This policy brief reviews literature on how social network analysis (SNA)—a method “effective in helping to understand how the stakeholders view one another, share information, cooperate, and take joint action”—has been used in low- and middle-income countries to evaluate UHC advocacy coalitions (Abbot et al., 2022). These findings were validated using experiences from coalition members in Nigeria advocating for UHC. The literature suggests that factors contributing to network success include network cohesion, decentralized network structure, collective action, strong transparency and trust between actors within an advocacy network, and clear communication and collaboration around advocacy objectives and the roles of all involved. The interviews suggest these themes are present in Nigeria as well. Using SNA thinking to conduct interviews in Nigeria, the authors highlighted specific strengths and areas of growth for advocacy coalitions for UHC. La réalisation de la couverture santé universelle (CSU) nécessite l'engagement d'un large éventail d'acteurs, notamment les décideurs politiques, la société civile et les universitaires. Dans les pays à faible et revenu intermédiaire, susciter un élan parmi les parties prenantes peut être difficile avec des priorités concurrentes et un financement limité. Les coalitions de plaidoyer - des groupes d'organisations partageant les mêmes idées se regroupant pour atteindre un objectif commun - ont été utilisées pour réaliser la CSU ; cependant, l'efficacité des coalitions de plaidoyer pour la CSU n'est pas bien comprise. Ce document politique passe en revue la littérature sur la manière dont l'analyse des réseaux sociaux (ARS) - une méthode "efficace pour aider à comprendre comment les parties prenantes se perçoivent mutuellement, partagent des informations, coopèrent et prennent des mesures conjointes" - a été utilisée dans les pays à faible et revenu intermédiaire pour évaluer les coalitions de plaidoyer pour la CSU (Abbot et al., 2022). Ces résultats ont été validés à l'aide des expériences des membres de la coalition au Nigéria plaidant en faveur de la CSU. La littérature suggère que les facteurs contribuant au succès du réseau comprennent la cohésion du réseau, la structure décentralisée du réseau, l'action collective, une transparence et une confiance solides entre les acteurs au sein d'un réseau de plaidoyer, et une collaboration autour des objectifs de plaidoyer et des rôles de tous les intervenants. Les entretiens suggèrent que ces thèmes sont également présents au Nigéria. En utilisant la pensée de l'ARS pour mener des entretiens au Nigéria, les auteurs ont mis en évidence des forces spécifiques et des domaines de croissance pour les coalitions de plaidoyer pour la CSU.
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Pietrobelli, Carlo, and Elisa Giuliani. Social Network Analysis Methodologies for the Evaluation of Cluster Development Programs. Inter-American Development Bank, November 2011. http://dx.doi.org/10.18235/0008963.

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Cluster development programs (CDPs) have been adopted widely in many countries worldwide. Many such programs aim to promote economic development by forming and strengthening inter-organizational networks. Despite their widespread diffusion, we know very little about CDP outputs or the impact CDPs have on host regions and their populations. Evaluation studies are beginning to appear, but the overall concern is that a distinct evaluation concept and method with a focus on CDPs is not yet available. The objective of this paper is to address this limitation, by proposing a novel methodological approach in the evaluation of CDPs based on the application of concepts and methods of social network analysis (SNA).
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