Journal articles: 'Social networks'

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Dunbar, Robin. "Social networks: Human social networks." New Scientist 214, no. 2859 (April 2012): iv—v. http://dx.doi.org/10.1016/s0262-4079(12)60856-2.

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Trevillion, S. "Social work, social networks and network knowledge." British Journal of Social Work 30, no. 4 (August 1, 2000): 505–17. http://dx.doi.org/10.1093/bjsw/30.4.505.

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Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (August 2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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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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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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Zenkovich, K., T. Zhylkybayev, S. Kaysanov, and T. Ustinova. "APPLYING SOCIAL MINING RESULTS FROM OPEN SOCIAL NETWORKS." Bulletin of Shakarim University. Technical Sciences 1, no. 2(14) (June 29, 2024): 5–10. http://dx.doi.org/10.53360/2788-7995-2024-2(14)-1.

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TThe advent of web-based communities and social networking sites has resulted in a massive amount of social networking data that is embedded with rich sets of meaningful social media knowledge. Social network analysis and the study of social structures using networks and graph theory help to find a systematic method or process for studying social networks. The article reveals the concept of intellectual analysis of social networks. The key aspect of the article is the application of the results of social network analysis to various branches of human activity.Describes the benefits of using Social Mining to identify patterns in big data. Using Social Mining mechanisms, you can find non-trivial and, at first glance, non-obvious patterns in large volumes of information. The article provides examples of software that can be used to quickly collect and analyze data from social networks. Analytics services simplify work and increase opportunities on social networks. Social network analysis provides an effective system for discovering and interpreting online social connections.Social network analytics goes beyond counting likes, reposts and links. This is a comprehensive indepth data analysis that helps to understand what attracts more attention or guide users when accessing the brand through social networks.

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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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Fuentes Cancell, Dieter Reynaldo, Odiel Estrada Molina, and Nilda Delgado Yanes. "Las redes sociales digitales: una valoración socioeducativa. Revisión sistemática." Revista Fuentes 1, no. 23 (2021): 41–52. http://dx.doi.org/10.12795/revistafuentes.2021.v23.i1.11947.

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The future and already current industrial revolution 4.0 demand the introduction of a digital transformation in the higher education contributing to the formation of competent professionals, for which, they are evidenced in the pedagogies and emergent technologies, an increase of didactic experiences in the use of the digital social networks. In this research a systematic review to identify current trends in the use of online social networks for educational purposes are performed. PRISMA protocol was used and analyzed 79 studies present in the database Scopus. In the systematic review, the following questions are answered: What types of designs predominate in the scientific literature? What is the dependentindependent pairs of variables? And What are the current trends in the use of digital social networks for educational purposes? As a result of this research, Facebook is reaffirmed as the social network most used by educators and the need for the integrated and varied use of these networks. It concludes with the defense of the following trends: university institutional communication policies from the curricular levels; the development of creativity, cultural convergence and media diversification; educational innovation; media culture and academic digital identity

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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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Banham, Gary. "Social networks." Philosophers' Magazine, no. 50 (2010): 22–23. http://dx.doi.org/10.5840/tpm20105038.

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Yevin, Igor Alexeevich, and Timur F. Khabibullin. "Social networks." Computer Research and Modeling 4, no. 2 (June 2012): 423–30. http://dx.doi.org/10.20537/2076-7633-2012-4-2-423-430.

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Menezes de Oliveira, Waldma Maíra, Gabriele Maria Muniz da Silva, and Ivanilde Apoluceno de Oliveira. "SOCIAL NETWORKS:." Revista Pedagógica 23 (September 5, 2021): 1–21. http://dx.doi.org/10.22196/rp.v22i0.6322.

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As relações estabelecidas no meio social produzem redes invisíveis que apresentam estrutura maleável e dinâmica. As redes sociais de interação, ou social networks, dentro de um contexto fechado de informantes, revelam influências quando estudadas em vista a um objetivo. Desenvolvida no município de Cametá-PA, a pesquisa investiga o comportamento das redes sociais de interação dentro do processo de constituição identitária de três sujeitos surdos. A fim de questionar que influências as redes sociais de interação têm neste processo, o estudo apresenta como objetivo específico: a) descrever a configuração das conexões da rede social de interação dos sujeitos surdos e b) compreender a influência das relações sociais no processo de constituição identitária dos sujeitos surdos. Tecida na interface da Educação Especial, na perspectiva Inclusiva e da Educação do Campo, o trabalho apoia-se em linhas teóricas da sociologia como Bauman (2005), Hall (2015), Goffman (2017) e articula-se no campo educacional. A partir do estudo de caso, a pesquisa configura-se com natureza quanti-qualitativa, cujos objetivos apresentam características descritivas. Os métodos utilizados para a coleta de dados foram os questionários semiestruturados e a análise das redes sociais. Os resultados apontam redes com características densas e frouxas, laços multiplex e uniplex, bem como atitudes que influenciam a constituição identitária de surdos e geram estigmas sociais. As conclusões apontam para novas pesquisas no campo da teoria das redes sociais de interação para verificar cruzamentos de redes dentro de um cenário amazônico tradicional marcado por culturas, ritmos de desenvolvimento e relação de poder.

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Gradoselskaya, Galina. "Social Networks." Journal of Economic Sociology 2, no. 1 (2001): 142–50. http://dx.doi.org/10.17323/1726-3247-2001-1-142-150.

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Beard, Margaret L. "Social networks." Psychosocial Rehabilitation Journal 16, no. 2 (1992): 111–16. http://dx.doi.org/10.1037/h0095695.

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Fox, Dirk. "Social networks." Datenschutz und Datensicherheit - DuD 33, no. 1 (January 2009): 53. http://dx.doi.org/10.1007/s11623-009-0011-9.

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Tippin, Stephanie, and Lorene Arnold. "Social Networks." Nurse Educator 37, no. 4 (2012): 148–49. http://dx.doi.org/10.1097/nne.0b013e31825a8770.

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Pagani, Margherita, and Charles Hofacker. "Use and Participation in Virtual Social Networks." International Journal of Virtual Communities and Social Networking 2, no. 1 (January 2010): 1–17. http://dx.doi.org/10.4018/jvcsn.2010010101.

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Managers are increasingly interested in the social web, as it provides numerous opportunities for strengthening and expanding relationships with customers, but the network processes that lead to these user-based assets are poorly understood. In this paper, the authors explore factors influencing use and participation in virtual social networks. They also discuss unusual drivers and inhibitors present with virtual social networksâ€”highlighted by the presence of positive network externalities and fears that the content will be misused. The authors offer hypotheses stemming from a model of how these factors work together, test the model with a dataset collected from two different virtual social networks, and discuss the implications of this work. The findings offer managers insights on how to nurture Web 2.0 processes.

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R, Vaishnavi, Athira C, Pradeep C, Sankalpkumar Sankalpkumar, Eashwara Prasanna, and Dr Srikanth V. "Energy Efficiency in Blockchain Social Networks." International Journal of Research Publication and Reviews 5, no. 3 (March 2, 2024): 819–25. http://dx.doi.org/10.55248/gengpi.5.0324.0632.

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Fogli, Alessandra, and Laura Veldkamp. "Germs, Social Networks, and Growth." Review of Economic Studies 88, no. 3 (April 2, 2021): 1074–100. http://dx.doi.org/10.1093/restud/rdab008.

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Abstract Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero. The reason is that networks diffuse both ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

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Mohan, Seshadri, Nitin Agarwal, and Ashutosh Dutta. "Social networks meet mobile networks." IEEE Communications Magazine 50, no. 6 (June 2012): 72–73. http://dx.doi.org/10.1109/mcom.2012.6211488.

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Downes, Stephen. "Semantic networks and social networks." Learning Organization 12, no. 5 (October 2005): 411–17. http://dx.doi.org/10.1108/09696470510700394.

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Wellman, B. "Computer Networks As Social Networks." Science 293, no. 5537 (September 14, 2001): 2031–34. http://dx.doi.org/10.1126/science.1065547.

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Litwin, Howard, and Gail K. Auslander. "Between Social Networks and Formal Social Services." Ageing and Society 8, no. 3 (September 1988): 269–85. http://dx.doi.org/10.1017/s0144686x00006942.

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ABSTRACTThis article reports a study of the social networks of recent applicants to the social welfare bureaux of Jerusalem. Beyond review of the relevant study variables as reflected in the literature, and an overview of Israeli social services for the aged, the article addresses why the social networks of the elderly claimants turned to formal assistance. The study revealed that networks were most likely to be the source of referral to social services when the applicant was frail, and the presenting problem was the need for institutional care. Multivariate analysis revealed that the degree of network-initiated contact with the social worker was significantly related to these same factors. Networks of the elderly seemed to turn to formal social services as a substitute for their own caregiving, rather than as a source for interweaving informal and formal care. The implications of the findings for social service policy are discussed.

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Daly, Alan J., Yi-Hwa Liou, Miguel Del Fresno, Martin Rehm, and Peter Bjorklund. "Educational Leadership in the Twitterverse: Social Media, Social Networks, and the New Social Continuum." Teachers College Record: The Voice of Scholarship in Education 121, no. 14 (November 2019): 1–20. http://dx.doi.org/10.1177/016146811912101404.

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Leadership is one of the most examined concepts in the literature, and while the study of social networks is also gaining interest, the intersection between leadership and online social networks has received limited attention. The key notion underlying most leadership research is that the behaviors or attributes of a leader matter for a variety of outcomes. While offering valuable insights, this dominant view of leadership behavior and attributes underestimates the impact of social networks. Scholars are increasingly recognizing the importance of social processes involved in leading. Leadership has often been conceptualized as a process of influence toward an outcome. Social relationships through networks may provide leaders with the necessary infrastructure to access resources in achieving outcomes. Leadership from a network perspective emphasizes the interdependence reflected by a network of ties, which may ultimately moderate, influence, or determine the activity and movement of practices and knowledge.

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van Ijzendoorn, Marinus H. "Attachment in Social Networks: Toward an Evolutionary Social Network Model." Human Development 48, no. 1-2 (2005): 85–88. http://dx.doi.org/10.1159/000083218.

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Bilò, Davide, Tobias Friedrich, Pascal Lenzner, Stefanie Lowski, and Anna Melnichenko. "Selfish Creation of Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5185–93. http://dx.doi.org/10.1609/aaai.v35i6.16655.

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Understanding real-world networks is a core research endeavor within the last two decades. Network Creation Games are a promising approach for this from a game-theoretic perspective. In these games, selfish agents corresponding to nodes in a network strategically decide which links to form to optimize their centrality. Many versions have been introduced and analyzed, but none of them fits to modeling the evolution of social networks. In real-world social networks connections are often established by recommendations from common acquaintances or by a chain of such recommendations. Thus establishing and maintaining a contact with a friend of a friend is easier than connecting to complete strangers. This explains the high clustering, i.e., the abundance of triangles, in real-world social networks. We propose and analyze a network creation model inspired by real-world social networks. In our model edges are formed via bilateral consent of both endpoints and the cost for establishing and maintaining an edge is proportional to the distance of the endpoints before establishing the connection. We provide results for generic cost functions which essentially only must be convex functions in the distance of the endpoints without the respective edge. For this broad class of cost functions we provide many structural properties of equilibrium networks and prove (almost) tight bounds on the diameter, the Price of Anarchy and the Price of Stability. Moreover, as a proof-of-concept we show via experiments that the created equilibrium networks of our model indeed closely mimic real-world social networks. We observe degree distributions that seem to follow a power-law, high clustering, and low diameters. This can be seen as a promising first step towards game-theoretic network creation models that predict networks featuring all core real-world properties.

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Watkins, Susan Cotts. "Social Networks and Social Science History." Social Science History 19, no. 3 (1995): 295–311. http://dx.doi.org/10.1017/s0145553200017387.

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The title of this presidential address reflects the happy conjunction of my particular interest in social networks and the network structure of the Social Science History Association. My talk will be brief, because I want to reserve most of this “presidential picnic” for the panel that the program chair, Donna Gabaccia, organized. Last year's president, Eric Monkkonen (1994: 166), in his history of the institution of the SSHA, called our meetings “a venue for scholars from different disciplines to learn to talk to one another.” That we have this annual opportunity for conversations is due to the work of our networks that organize the sessions that attract us to the meetings; to program chairs—this year, Donna—who create a program from these sessions; and to our executive director, Erik Austin, whose ability and diligence keeps the organization going from year to year.

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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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Machuco Rosa, António. "Mimesis, network theory and digital social networks." Xiphias Gladius Revista interdisciplinar de Teoría Mimética, no. 1 (December 19, 2018): 93–111. http://dx.doi.org/10.32466/eufv-xg.2018.1.387.93-111.

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We analyse the formal theory of networks and the new digital media from the perspective of mimetic theory. It is emphasised that the spatial form of the new media is characterised by distribution functions that are power laws. We show that this spatial form can be derived from the presence of mimetic desire in digital social networks. Finally, we show that network theory allows the representation of the scapegoat mechanism as it was analysed by René Girard.

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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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Gilles, Robert, Tabitha James, Reza Barkhi, and Dimitrios Diamantaras. "Simulating Social Network Formation." International Journal of Virtual Communities and Social Networking 1, no. 4 (October 2009): 1–20. http://dx.doi.org/10.4018/jvcsn.2009092201.

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Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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Колесников, M. Kolesnikov, Киба, and M. Kiba. "Management in Social and Professional Networks." Administration 3, no. 3 (September 17, 2015): 68–72. http://dx.doi.org/10.12737/13340.

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The extended idea about “professional network” has been developed based on the concept of “social network”. The main features and tasks of professional social networks have been revealed. The task of management in social and professional networks which demanded formalization of criteria and models of their functioning has been set. Professional social network has been represented as a graph model. Known models of influence in social and professional networks have been considered and described in detail from the point of view of their analytical representation. The main features of professional networks influence on society and production have been revealed. Possibility for mass distribution of information by means of professional social networks has been analyzed. A mechanism for network agents’ opinion formation on the basis of authoritative opinion and a trust vector has been revealed.

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Kamoru, Balogun Abiodun, Azmi Bin Jaafar, Masrah Azrifah Azmi Murad, and Marzanah A. Jabar. "Understanding Security Threats in Spam Detection on Social Networks." Circulation in Computer Science 2, no. 5 (June 20, 2017): 18–22. http://dx.doi.org/10.22632/ccs-2017-252-19.

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Social network has become a very popular way for internet users to communicate and interact online. The socia; networks provide a platform to maintain a contact with friends. Increasing social network’s popularity allows all of them to collect large amounts of personal details about their users. Globally, the issue of identifying spammers have received great attention due to its practical relevance in the field of social network analysis. Social network community users are fed with irrelevant information while surfing, due to spammer's activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. The aim of this paper is to examine previous works in the field of spam detection in social networks, the study attempts to review various spam detection frameworks which details about the detection and elimination of spam's in various sources, By classification and Clustering Method of spam detection and by raising security awareness among the users of social networks and stake holders , by prescribing a strategic approach or data mining approach for analyzing the nature of spam detection on social networks.

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Fershtman, Chaim, and Dotan Persitz. "Social Clubs and Social Networks." American Economic Journal: Microeconomics 13, no. 1 (February 1, 2021): 224–51. http://dx.doi.org/10.1257/mic.20180143.

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We present a strategic network formation model based on membership in clubs. Individuals choose affiliations. The set of all memberships induces a weighted network where two individuals are directly connected if they share a club. Two individuals may also be indirectly connected using multiple memberships of third parties. Individuals gain from their position in the induced network and pay membership fees. We study the club congestion model where the weight of a link decreases with the size of the smallest shared club. A trade-off emerges between the size of clubs, the depreciation of indirect connections, and the membership fee. (JEL D71, D85, Z13)

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Brugha, T. S. "Social support and social networks." Current Opinion in Psychiatry 2, no. 2 (April 1989): 278–82. http://dx.doi.org/10.1097/00001504-198904000-00017.

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diani, mario. "social networks and social movements." European Political Science 15, no. 2 (August 14, 2015): 266–67. http://dx.doi.org/10.1057/eps.2015.48.

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Hardesty, Jared Ross. "Social Networks and Social Worlds." Journal of Global Slavery 3, no. 3 (August 8, 2018): 234–60. http://dx.doi.org/10.1163/2405836x-00303003.

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Abstract This essay argues that the “slave community” paradigm obfuscates alternative lived experiences for enslaved men and women, especially those living in the urban areas of the early modern Atlantic world, and uses eighteenth-century Boston as a case study. A bustling Atlantic port city where slaves comprised between ten and fifteen percent of the population, Boston provides an important counterpoint. Slaves were a minority of residents, lived in households with few other people of African descent, worked with laborers from across the socio-economic spectrum, and had near constant interaction with their masters. Moreover, slavery in Boston reached its zenith before the American Revolution, meaning older, pre-revolutionary and early modern notions of social order—hierarchy, deference, and dependence—structured their society and everyday lives. These factors imbricated enslaved Bostonians in the broader society. Boston’s slaves inhabited multiple “social worlds” where they fostered a rich tapestry of relations and forms of resistance.

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Pilisuk, Marc. "Social Networks and Social Exclusion." Contemporary Sociology: A Journal of Reviews 34, no. 1 (January 2005): 34–36. http://dx.doi.org/10.1177/009430610503400121.

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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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Zhang, Lianming, Aoyuan Peng, and Jianping Yu. "Reconfiguration and Search of Social Networks." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/391782.

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Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based onk-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency.

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Jenssen, Jan Inge. "Social Networks, Resources and Entrepreneurship." International Journal of Entrepreneurship and Innovation 2, no. 2 (June 2001): 103–9. http://dx.doi.org/10.5367/000000001101298846.

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The question to be addressed in this study is how social networks and entrepreneurial resources relate to and impact on entrepreneurship. This question has been answered through an empirical investigation carried out in Norway. The explanatory variables applied in the study capture up to 45.6% of the variability of start-up success. The results show that social networks are important as channels for resources. The introduction of resources as an intervening variable considerably increases the explanatory power of the network approach. The study also indicates that it is useful to distinguish between the network developed before the entrepreneurial process and the network developed through the process.

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Board, Simon, and Moritz Meyer-ter-Vehn. "Learning Dynamics in Social Networks." Econometrica 89, no. 6 (2021): 2601–35. http://dx.doi.org/10.3982/ecta18659.

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This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

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Greve, Arent, and Janet W. Salaff. "Social Networks and Entrepreneurship." Entrepreneurship Theory and Practice 28, no. 1 (January 2003): 1–22. http://dx.doi.org/10.1111/1540-8520.00029.

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We study network activities of entrepreneurs through three phases of establishing a firm in four countries. Entrepreneurs access people in their networks to discuss aspects of establishing and running a business. We find that entrepreneurs build networks that systematically vary by the phase of entrepreneurship, analyzing number of their discussion partners, and the time spent networking. Entrepreneurs talk with more people during the planning than other phases. Family members are present in their networks in all phases, particularly among those who took over an existing firm. However, women use their kin to a larger extent than men, and even more than men when they take over an existing firm. Experienced entrepreneurs have the same networking patterns as novices. Moreover, these networking patterns are the same in all countries. However, there are country differences in size of discussion networks and time spent networking.

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Abrams, Marshall. "Maintenance of cultural diversity: Social roles, social networks, and cognitive networks." Behavioral and Brain Sciences 37, no. 3 (June 2014): 254–55. http://dx.doi.org/10.1017/s0140525x13002811.

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AbstractSmaldino suggests that patterns that give rise to group-level cultural traits can also increase individual-level cultural diversity. I distinguish social roles and related social network structures and discuss ways in which each might maintain diversity. I suggest that cognitive analogs of “cohesion,” a property of networks that helps maintenance of diversity, might mediate the effects of social roles on diversity.

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Zubarieva, Mariia Anatoliivna, and Serhiy Volodymyrovych Shturkhetskyy. "MARKETING FEATURES OF PROMOTION TACTIСS OF SOCIAL BUSINESS IN SOCIAL NETWORKS." SCIENTIFIC BULLETIN OF POLISSIA 2, no. 1(13) (2018): 205–9. http://dx.doi.org/10.25140/2410-9576-2018-2-1(13)-205-209.

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Bruce, Ann, Cornell Jackson, and Chrysa Lamprinopoulou. "Social networks and farming resilience." Outlook on Agriculture 50, no. 2 (January 12, 2021): 196–205. http://dx.doi.org/10.1177/0030727020984812.

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The resilience of food systems, including agricultural systems, has become a high profile issue in the face multiple disease, environmental and social challenges. Much of agriculture takes place in remote locations where social networks, or connections between individual actors, have been implicated in increasing resilience. We examine a case study of Orkney, Scotland, a remote rural location, using interviews and Social Network Analysis. This case study provides evidence indicative of resilient patterns of social networks, emphasising the importance of schools, transport links and livestock markets in creating and maintaining these networks. These domains are rarely included in agricultural policy, highlighting the need for wider framing of questions. Our research suggests Social Network Analysis is a fruitful avenue for investigating resilience of agricultural systems that can identify hitherto hidden elements.

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Fani, Hossein, and Ebrahim Bagheri. "Community detection in social networks." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630001. http://dx.doi.org/10.1142/s2425038416300019.

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Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.

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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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Sokolovska, Valentina. "Social networks, social capital and social status." Socioloski pregled 45, no. 2 (2011): 221–34. http://dx.doi.org/10.5937/socpreg1102221s.

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Schafft, Kai, and David Brown. "Social capital, social networks, and social power." Social Epistemology 17, no. 4 (January 2003): 329–42. http://dx.doi.org/10.1080/0269172032000151795.

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Journal articles on the topic 'Social networks'

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