Journal articles: 'Social Networks 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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Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (April 2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour 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. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (September 26, 2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.

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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.

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Thovex, Christophe, and Francky Trichet. "Semantic social networks analysis." Social Network Analysis and Mining 3, no. 1 (February 24, 2012): 35–49. http://dx.doi.org/10.1007/s13278-012-0055-y.

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Andryani, Ria, Edi Surya Negara, Rezki Syaputra, and Deni Erlansyah. "Analysis of Academic Social Networks in Indonesia." Qubahan Academic Journal 3, no. 4 (December 9, 2023): 409–21. http://dx.doi.org/10.58429/qaj.v3n4a289.

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Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.

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Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.

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Skvoretz, John. "Pas de Deux: Social Networks and Network Analysis." Contemporary Sociology: A Journal of Reviews 37, no. 5 (September 2008): 423–26. http://dx.doi.org/10.1177/009430610803700511.

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Himelboim, Itai, Marc A. Smith, Lee Rainie, Ben Shneiderman, and Camila Espina. "Classifying Twitter Topic-Networks Using Social Network Analysis." Social Media + Society 3, no. 1 (January 2017): 205630511769154. http://dx.doi.org/10.1177/2056305117691545.

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As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.

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Schwartz, Daniel M., and Tony (D.A.) Rouselle. "Using social network analysis to target criminal networks." Trends in Organized Crime 12, no. 2 (October 24, 2008): 188–207. http://dx.doi.org/10.1007/s12117-008-9046-9.

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Bharsakle, Pragati Dnyaneshwar. "Social Networks for Threat Perception and Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5073–77. http://dx.doi.org/10.22214/ijraset.2021.35911.

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In the current era of massive knowledge, high volumes of valuable knowledge is simply collected and generated. Social networks square measure samples of generating sources of those huge knowledge. Users in these social networks square measure usually coupled by some interdependency like friendly relationship. As these huge social networks continue to grow, there square measure things during which Associate in Nursing individual user needs to seek out common teams of friends so he will suggest a similar teams to alternative users. Many users of social Network are not aware about the number of security risks in networks such as identity theft, privacy violations, sexual harassment etc,. Recent studies says that most of the social network users expose their personal information like their date of birth, email address, phone number, relationship status. If this type of data reached to the wrong person, then person used that information to harm the users. If the children are users of social network, then these risks become serious. In this paper we present an alternative data analytic solution by using pattern matching solution.

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Arif, Tasleem. "The Mathematics of Social Network Analysis: Metrics for Academic Social Networks." International Journal of Computer Applications Technology and Research 4, no. 12 (November 26, 2015): 889–93. http://dx.doi.org/10.7753/ijcatr0412.1003.

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Kim, Jooho, and Makarand Hastak. "Social network analysis: Characteristics of online social networks after a disaster." International Journal of Information Management 38, no. 1 (February 2018): 86–96. http://dx.doi.org/10.1016/j.ijinfomgt.2017.08.003.

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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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Hu, Xuerong. "Analysis Of the Influence of The Social Network on Childhood Obesity." Highlights in Business, Economics and Management 23 (December 29, 2023): 1206–11. http://dx.doi.org/10.54097/2tr0sm30.

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Childhood obesity is a pressing global public health concern shaped by genetic, nutritional, lifestyle, and socio-economic factors. This study examines the influence of distinct social networks—family, neighborhood, internet, and social-cultural networks—on childhood obesity. To assess each network's impact, scrutinizing their contributing factors and interactions to inform effective policy development. The study employs a comprehensive literature review, incorporating data from Chinese and English sources. It utilizes the Social Ecological Model (SEM) to analyze the impact of the family network on childhood obesity and examines various factors within the neighborhood environment. The study also investigates the influence of internet social networks, particularly through social media influencer marketing, and explores the impact of social and cultural networks, with a focus on China and the United States. The family network significantly influences childhood obesity, with factors like parental characteristics, age, race, and gender exhibiting associations. Proximity to food outlets and physical activity facilities within the neighborhood plays a key role. Internet social networks can sway children's dietary choices, with influencers promoting unhealthy foods leading to increased consumption. Social and cultural networks in different contexts contribute, influenced by traditional beliefs and migration dynamics. This study underscores the intricate and multifaceted influence of social networks on childhood obesity, highlighting the roles played by each network in shaping children's weight status and dietary choices. Understanding these network dynamics is vital for designing effective prevention and intervention strategies.

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Beigi, Ghazaleh, Jiliang Tang, and Huan Liu. "Signed Link Analysis in Social Media Networks." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 539–42. http://dx.doi.org/10.1609/icwsm.v10i1.14793.

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Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially distinct from those of unsigned networks, thus we need dedicated efforts on link analysis in signed social networks. In this paper, following social theories in link analysis in unsigned networks, we adopt three social science theories, namely Emotional Information, Diffusion of Innovations and Individual Personality, to guide the task of link analysis in signed networks.

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Gemici, Kurtuluş, and Anthony Vashevko. "Visualizing Hierarchical Social Networks." Socius: Sociological Research for a Dynamic World 4 (January 1, 2018): 237802311877298. http://dx.doi.org/10.1177/2378023118772982.

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The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between groups containing the nodes in the network. As such, the proposed visualization method facilitates theoretical and empirical analysis of social structures by algorithmically combining information from the underlying network with the information from the hierarchical structure of the network. The authors illustrate the proposed method with social networks examined through cohesive blocking and k-core decomposition.

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Lu, Yingjie, Xinwei Wang, Lin Su, and Han Zhao. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities." Mathematics 11, no. 21 (October 24, 2023): 4412. http://dx.doi.org/10.3390/math11214412.

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Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

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Макатов, Е. К., А. О. Актаева, У. Б. Кусаинова, and Н. М. Аусилова. "Social networks: history and analysis." Bulletin of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series 127, no. 1 (2019): 60–66. http://dx.doi.org/10.32523/2616-7263-2019-126-1-60-66.

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Machová, Kristína, and Tomáš Rakuščinec. "Dynamic Analysis of Social Networks." American Journal of Intelligent Systems 2, no. 6 (December 1, 2012): 148–56. http://dx.doi.org/10.5923/j.ajis.20120206.02.

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França Batista, Andre Luiz, Carlos Henrique Da Silveira Campos, and Daniel Ramos Pimentel. "Colloquiality analysis on social networks." International Journal for Innovation Education and Research 8, no. 4 (April 1, 2020): 369–90. http://dx.doi.org/10.31686/ijier.vol8.iss4.2295.

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Social network sites are present in a constant way in society. The manner people write in social network sites has their dynamism because of the speed information are replicated, the reach publications may have, network sites’ peculiarities and the seek for fame. This generates linguistic constructions that are not in accord with the standard norm of the Portuguese language. This quantitative work aims to relate the use of colloquial constructions on Twitter with user's popularity, posts popularity and other specific factors of this social network. This analysis was made using regular expressions, dictionaries, and frequency distribution to identify colloquial constructions. A support system was developed to perform analysis, management, and mining.

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Arenas, A., L. Danon, A. D�az-Guilera, P. M. Gleiser, and R. Guimer�. "Community analysis in social networks." European Physical Journal B - Condensed Matter 38, no. 2 (March 1, 2004): 373–80. http://dx.doi.org/10.1140/epjb/e2004-00130-1.

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O’Malley, A. James, and Peter V. Marsden. "The analysis of social networks." Health Services and Outcomes Research Methodology 8, no. 4 (October 21, 2008): 222–69. http://dx.doi.org/10.1007/s10742-008-0041-z.

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Batagelj, Vladimir. "Semirings for social networks analysis." Journal of Mathematical Sociology 19, no. 1 (May 1994): 53–68. http://dx.doi.org/10.1080/0022250x.1994.9990135.

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Hernández-Carrión, Carlos, Carmen Camarero-Izquierdo, and Jesús Gutiérrez-Cillán. "The internal mechanisms of entrepreneurs’ social capital: A multi-network analysis." BRQ Business Research Quarterly 23, no. 1 (January 2020): 234094442090104. http://dx.doi.org/10.1177/2340944420901047.

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The present work addresses how and to what extent the personal and professional relationship networks of small-scale local entrepreneurs help improve their scarce resource endowments. Adopting a mechanistic and functioning oriented view of social capital, the paper suggests the existence of two different mechanisms which may explain the enrichment and entrepreneurial exploitation processes of social capital’s resources: (1) the resource mechanism, based on a network’s size and diversity, providing quantity and variety of social capital resources, and (2) the exchange mechanism, based on a network’s cohesion and relational quality, favouring the interchangeability of these resources among network members. The empirical study individually explores both the personal and professional networks of 958 Spanish entrepreneurs. Findings reveal how the two mechanisms are necessary and mutually complementary, although the resource mechanism proves more advantageous when exploiting personal networks, whereas the exchange mechanism prevails in the case of professional networks. JEL CLASSIFICATION L14; L26; M13; M14

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Tekşen, Kerem, and Necati Cemaloğlu. "Mobbing and Social Network Analysis." Technium Social Sciences Journal 39 (January 8, 2023): 184–94. http://dx.doi.org/10.47577/tssj.v39i1.8214.

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The aim of this research is to find out the level of mobbing experience of teachers working in educational institutions and to determine the network characteristics of both the social networks of the organizations where mobbing behavior is common and the participants in these networks. The target population of the research consists of teachers working in a province in Türkiye. The sample of the population was determined by cluster sampling method. In total, 376 teachers in 30 schools were reached, but 11 questionnaires were removed during the pre-analysis data scanning phase, and the remaining 365 questionnaires were analyzed. “Negative Acts Questionnaire” and “Social Network Analysis Questionnaire” were used as data collection tools in the research. SPSS 21.0 and UCINET 6 statistical package programs were used for the analysis of the data obtained in the research, and "frequency", "mean" and "multi-network measurements" were used in data analysis. As a result of the research, it is determined that the average level of mobbing experience of teachers in the organizations participating in the research is low. In addition, three organizations where mobbing is common is determined and the social network structures of these organizations is examined. It is observed that the average degrees and network densities are generally low in these organizations. In addition, these organizations generally show a low level of transitivity. In addition, it is evaluated that some of the participants in the social networks of these organizations may be victims of mobbing, considering that they have a low overall degree. As a support to this finding, it is observed that the participants in question have higher internal and external closeness, low betweenness and low eigenvector values compared to other participants in the organization.

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Brass, Daniel J. "New Developments in Social Network Analysis." Annual Review of Organizational Psychology and Organizational Behavior 9, no. 1 (January 21, 2022): 225–46. http://dx.doi.org/10.1146/annurev-orgpsych-012420-090628.

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This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?”

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Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (June 20, 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.

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Sungkuk KIM and 이승택. "International Trade Networks of Arms using Social Network Analysis." Journal of International Trade & Commerce 11, no. 2 (April 2015): 599–613. http://dx.doi.org/10.16980/jitc.11.2.201504.599.

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Szilágyi, Győző Attila. "Exploration Knowledge Sharing Networks Using Social Network Analysis Methods." Economics & Sociology 10, no. 3 (October 2017): 179–91. http://dx.doi.org/10.14254/2071-789x.2017/10-3/13.

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Hoppe, Bruce, and Claire Reinelt. "Social network analysis and the evaluation of leadership networks." Leadership Quarterly 21, no. 4 (August 2010): 600–619. http://dx.doi.org/10.1016/j.leaqua.2010.06.004.

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Anusha, V. V. S. S., S. R. Patel, and Vinaya Kumar, H.M. "UNLOCKING AGRICULTURAL INFORMATION NETWORKS: A SOCIAL NETWORK ANALYSIS APPROACH." Gujarat Journal of Extension Education 36, no. 2 (December 25, 2023): 104–11. http://dx.doi.org/10.56572/gjoee.2023.36.2.0019.

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Indian extension system persistently encountered challenges related to the ineffective dissemination of agricultural information and lack of intensive private extension. Social Network Analysis (SNA) focuses on how actors are located in a network and studies their multidimensional relationship. The present study aims to explore the information-seeking patterns of the farmers using SNA and understand the effective and efficient means of communication that the extension system can tap further. Two districts (Anand and Vadodara) of Gujarat were conveniently selected from which two blocks and two villages each were purposively selected for the study. By employing random sampling, a total of 120 respondents were interviewed. The analysis using UCINET software elicited that respondents had 36 nodes contacted for attaining information for production aspects, with 27 and 23 nodes for protection and marketing, respectively. However, the density (0.553) and transitivity (0.705) were high for the protection network, implying a stronger connection between the nodes and having a lower average geodesic distance (1.450), conveying that the information dissemination related to protection aspects happens rapidly. Non-dominance of either public or private entities in the marketing network emphasized the need for attention. At the node level, input dealers were of significant value in the network, and the respondents of Vadodara had more accessibility to agriculture information than the Anand district. The study’s findings contribute significantly to the existing literature by shedding light on the challenges within the Indian extension system, emphasizing the importance of Social Network Analysis in understanding farmers’ information-seeking patterns and highlighting key areas for improvement in agricultural communication and information dissemination.

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Hayat, Tsahi (Zack), Ofrit Lesser, and Tal Samuel-Azran. "Gendered discourse patterns on online social networks: A social network analysis perspective." Computers in Human Behavior 77 (December 2017): 132–39. http://dx.doi.org/10.1016/j.chb.2017.08.041.

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Kridera, Stavroula, and Andreas Kanavos. "Exploring Trust Dynamics in Online Social Networks: A Social Network Analysis Perspective." Mathematical and Computational Applications 29, no. 3 (May 15, 2024): 37. http://dx.doi.org/10.3390/mca29030037.

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This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML models (e.g., KNN, SVM, Naive Bayes, Gradient Boosting, and Neural Networks), we predict connection strengths on Facebook, focusing on model performance metrics such as accuracy, precision, recall, and F1-score. Our methodology, executed in Python using the Anaconda distribution, unveils insights into trust formation and sustainability on social media, highlighting the potent application of ML in understanding these dynamics. Challenges, including the complexity of modeling social behaviors and ethical data use concerns, are discussed, emphasizing the need for continued innovation. Our findings contribute to the discourse on trust in social networks and suggest future research directions, including the application of our methodologies to other platforms and the study of online trust over time. This work not only advances the academic understanding of digital social interactions but also offers practical implications for developers, policymakers, and online communities.

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Linke, Knut, and Torben Friedrich. "Analysis of B-2-C Social Media Communication in Germany." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 3 (2018): 23–31. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.33.3002.

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In this research paper, social media marketing activities of social media accounts from German business are analyzed for the social networks Facebook, Twitter, Instagram, Xing and LinkedIn. As research objects were the context of the interaction, the used and targeted social network functionalities and the behavior of the companies selected. The selection of social media accounts for the research included companies which are currently member from the stock market indices DAX and MDAX, additional online and offline retail business, successful German sports clubs, celebrities and others businesses. Also and to be sure to evaluate high-class social media marketing, the German social media award winners from 2015-2017 were analyzed. Out of the results of those two analyses, the results were derivate. The results display different usage approaches between the researched networks and the business fields. As result of the research, several contexts approaches for social media posts are defined. The results contain suggestions for the standardization of those contexts and the different approaches how functions from social networks can be used for user interactions. That includes the targeted reactions and standardized reactions of user interactions.

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Lemay, David, Tenzin Doleck, and Christopher Brinton. "SLOAN: Social Learning Optimization Analysis of Networks." International Review of Research in Open and Distributed Learning 23, no. 4 (November 1, 2022): 93–122. http://dx.doi.org/10.19173/irrodl.v23i4.6484.

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Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

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Dao, Nhu-Ngoc, and Sungrae Cho. "Security Analysis of Social Networking Based Authentication in Infrastructureless PAC Networks." International Journal of Future Computer and Communication 8, no. 1 (March 2019): 1–5. http://dx.doi.org/10.18178/ijfcc.2019.8.1.530.

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Kataev, M. Yu, and V. V. Orlova. "Social media event data analysis." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 4 (December 25, 2020): 71–77. http://dx.doi.org/10.21293/1818-0442-2020-23-4-71-77.

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Social media analysis has become ubiquitous at a quantitative and qualitative level due to the ability to study content from open social networks. This content is a rich source of data for the construction and analysis of the interaction of social network users when forming various groups, used not only for statistical calculations, social areas of analysis, but also in trade or for the development of recommendation systems. The large number of social media users results in a huge amount of unstructured data (by time, type of communication, type of message and geographic location). This article aims to discuss the problem of analyzing social networks and obtaining information from unstructured data. The article discusses information extraction methods, well-known software products and datasets.

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Abdul Rauf, Siti Hajar, Siti Hajar Abu Bakar Ah, and Adi Fahrudin. "Social Change PostCOVID-19 in Malaysia: The Density of Social Network." Asian Social Work Journal 5, no. 2 (July 27, 2020): 1–5. http://dx.doi.org/10.47405/aswj.v5i2.136.

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The COVID-19 pandemic is a global health problem that has given the greatest challenge for humanity today. This paper discusses the social changes that have taken place on social network density after COVID-19 hit the world. The social network density discussed is based on Social Network Theory according to the current situation that has hit Malaysia. The methods used are based on document analysis and case analysis from official documents issued by the government. The analysis revealed that the COVID-19 pandemic has had a devastating impact on human health, social and economic. However, seen from the standpoint of social networks, the COVID-19 pandemic has led to the emergence of densities in social networks due to increased informal sector involvement in the formation of social networks. This means that, as the number of social networks is formed, the density of social network will increased as defined by Social Network Theory.

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Manning, Paul. "Madoff’s Ponzi investment fraud: a social capital analysis." Journal of Financial Crime 25, no. 2 (May 8, 2018): 320–36. http://dx.doi.org/10.1108/jfc-06-2017-0057.

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Purpose The social network analysis of criminal networks at both the ego and socio-centric level is well established. This purpose of this study is to expand this literature with a social capital analysis of a criminal network. The focus of the analysis will be the recent egregious investment fraud of Bernard L. Madoff Investment Securities (BLMIS). Design/methodology/approach This research involves a case study of the BLMIS financial fraud. The article uses a social capital theoretical lens, with archival sources taken from the court records of Madoff v. NY to include victim impact statements and the defendant’s Plea Allocution. Findings Financial crime literature can be expanded with a social capital analysis which facilitates a socio-economic analysis of ego-centric criminal networks. Research limitations/implications Each financial crime is of its time; however, there are recurring socio-economic network characteristics that could be applied to develop an understanding of criminal networks. Practical implications Any understanding of financial crime, including contemporary instances of criminal innovation, such as cyber-crime, can be enhanced with a social capital analysis of criminal networks. Originality/value A social capital analysis of financial crime draws attention to “human factors” in criminal networks that are integral to this form of crime.

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Smith, Edward Bishop, Raina A. Brands, Matthew E. Brashears, and Adam M. Kleinbaum. "Social Networks and Cognition." Annual Review of Sociology 46, no. 1 (July 30, 2020): 159–74. http://dx.doi.org/10.1146/annurev-soc-121919-054736.

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Social network analysis, now often thought of simply as network science, has penetrated nearly every scientific and many scholarly fields and has become an indispensable resource. Yet, social networks are special by virtue of being specifically social, and our growing understanding of the brain is affecting our understanding of how social networks form, mature, and are exploited by their members. We discuss the expanding research on how the brain manages social information, how this information is heuristically processed, and how network cognitions are affected by situation and circumstance. In the process, we argue that the cognitive turn in social networks exemplifies the modern conception of the brain as fundamentally reprogrammable by experience and circumstance. Far from social networks being dependent upon the brain, we anticipate a modern view in which cognition and social networks coconstitute each other.

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KC, Birendra, Duarte B. Morais, M. Nils Peterson, Erin Seekamp, and Jordan W. Smith. "Social network analysis of wildlife tourism microentrepreneurial network." Tourism and Hospitality Research 19, no. 2 (June 30, 2017): 158–69. http://dx.doi.org/10.1177/1467358417715679.

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Social networks are an important element of entrepreneurship. Entrepreneurs rely on social networks to access ideas, information, and resources to facilitate their entrepreneurial process. Strong and weak ties influence the entrepreneurial process in unique ways. This study utilized social network analysis approach to examine wildlife tourism microentrepreneurship through in-person structured interviews with 37 microentrepreneurs from North Carolina’s Pamlico Sound Region. Specifically, this study examined the extent of network ties, the type of support received from those network ties, and the process of creating and maintaining the business network ties. Weak ties were more prevalent than strong ties. Support was received in terms of marketing and advertising, information sharing, and product sponsorship. Weak ties were established through professional workshops and seminars or while working in the same territory, whereas reciprocity, togetherness, communication, and trust were identified as major factors to maintain weak ties. This study suggests that cognitive social capital factors (e.g. reciprocity, togetherness, and trust) can be highly important toward effective use of social networks, as well as to ensure entrepreneurial success.

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Wiedemann, Nina, Henry Martin, and Martin Raubal. "Unlocking social network analysis methods for studying human mobility." AGILE: GIScience Series 3 (June 10, 2022): 1–12. http://dx.doi.org/10.5194/agile-giss-3-19-2022.

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Abstract. Planning and operations in urban spaces are strongly affected by human mobility behavior. A better understanding of individual mobility is key to improve transportation systems and to guide the allocation of public space. Previous studies have discovered statistical laws of travel distances, but the topology of movement between places has received little attention. We propose to employ network modelling methods to analyze the effect of spatial and context attributes on individual movement patterns. The perspective of mobility as a network allows to explicitly regard dyadic dependencies of sequential location visits. Here, we consider two methods developed for social networks and provide a formulation of mobility networks to justify their applicability. First, we use the Multiple Regression Quadratic Assignment Procedure to test hypotheses on the influence of location attributes on mobility behavior. Secondly, Stochastic Actor-Oriented Models are applied to model the evolution of mobility networks over time. As a proof-of-concept study, we transform data from one GNSS-based and one check-in based dataset into mobility networks and present results from both methods. We find relations that appear for a majority of samples and thus seem inherent to mobility networks. The differences between individuals and the available datasets are further quantified and discussed. We conclude that the transfer of network modeling methods is an interesting opportunity to study network-related phenomena in geographic information science.

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Jazmin, Enriquez-Sanchez, Munoz-Rodriguez Manrrubio, J. Reyes Altamirano-Cardenas, and Gante Abraham Villegas-De. "Activation process analysis of the Localized Agri-food System using social networks." Agricultural Economics (Zemědělská ekonomika) 63, No. 3 (March 7, 2017): 121–35. http://dx.doi.org/10.17221/254/2015-agricecon.

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The objective of the work was to analyse the prevailing activation process of the Localized Agri-food System (LAS) by using social networks as a tool to value the pre-existing social capital. There were 27 producers of “Chiapas Cream Cheese” and the members of the formal cheese maker organization from the state of Chiapas, Mexico that were interviewed. By the means of cluster analysis and the graphic design of friendship, the kinship, the “compadrazgo” knowledge, the collaboration and cooperation networks, we concluded that the structural activation must transcend the formal creation of an organization. It is best to value and then mobilize the pre-existing social capital in a territory with a specific traditional know-how as a foundation to the structure and activation process of the LAS. Four actors were identified for their active participation in all analysed networks; these were the information diffusers and network structures. Weak links in the cheese maker organization favour the innovation adoption; whereas the strong links maintain the know-how.

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Williams, Trenton A., and Dean A. Shepherd. "Mixed Method Social Network Analysis." Organizational Research Methods 20, no. 2 (July 8, 2016): 268–98. http://dx.doi.org/10.1177/1094428115610807.

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This article outlines a mixed method approach to social network analysis combining techniques of organizational history development, inductive data structuring, and content analysis to offer a novel approach for network data construction and analysis. This approach provides researchers with a number of benefits over traditional sociometric or other interpersonal methodologies including the ability to investigate networks of greater scope, broader access to diverse social actors, reduced informant bias, and increased capability for longitudinal designs. After detailing this approach, we apply the method on a sample of 143 new ventures and suggest opportunities for general application in entrepreneurship, strategic management, and organizational behavior research.

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Klyucharev, P., and M. Basarab. "Spectral Analysis Methods of Social Networks." Science and Education of the Bauman MSTU 17, no. 05 (May 3, 2017): 168–77. http://dx.doi.org/10.7463/0517.0001159.

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Mussina, A., S. Aubakirov, and P. Trigo. "PARAMETRIZED EVENT ANALYSIS FROM SOCIAL NETWORKS." Scientific Journal of Astana IT University 10 (June 30, 2022): 89–96. http://dx.doi.org/10.37943/tsyv3590.

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The growth of data in social networks facilitate demand for data analysis. The field of event detection is of increasing interest to researchers. Events from real life are actively discussed in the virtual space. Event detection results can be used in a variety of applications, from digital marketing to collecting data about natural disasters. Thereby, researchers face the emergence of new algorithms along with the improvement of existing solutions in the event detection field. This paper proposes improvements to the SEDTWik (Segment-based Event Detection from Tweets using Wikipedia) algorithm. The SEDTWik algorithm is designed to detect events without contextual guidance. The overall SEDTWik detection process excludes the perspective of a topic, or multi-topic, guided (or semi-supervised) event detection approach. As a result, some interesting narrowly focused events are not detected as they are weakly relevant in a broader context (e.g., Wikipedia) although acquiring relevance within a conditioned context. Therefore, there is a need for an adaptive perspective where data is to be analysed against a set of narrower topics of interest. This paper shows that SEDTWik gains expressive power after being extended with multi-topic semi-supervision. The evaluation of the current proposal uses the well-known corpora with labeled events, Events2012. In the Events2012 dataset used notation category for events, meaning that events are combined by a certain topic. SEDTWik with topic dictionaries was checked across all categories. In the main part of the article, it is also explained the process of topic dictionary construction from Events2012 labeled tweets. At this stage of the research, in all tasks unigrams were used. SEDTWik with dictionaries showed improved accuracy, and more events were found within a certain category.

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Zheng, Wei, Qian Pan, Chen Sun, Yu-Fan Deng, Xiao-Kang Zhao, and Zhao Kang. "Fractal Analysis of Mobile Social Networks." Chinese Physics Letters 33, no. 3 (March 2016): 038901. http://dx.doi.org/10.1088/0256-307x/33/3/038901.

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Zheng, Wei, Zifeng Zhang, and Yufan Deng. "Multifractal analysis of mobile social networks." EPL (Europhysics Letters) 119, no. 6 (September 1, 2017): 66006. http://dx.doi.org/10.1209/0295-5075/119/66006.

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Journal articles on the topic 'Social Networks Analysis'

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