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The Emergence and Stability of Groups in Social Networks
Swiss Fed Inst Technol, Switzerland.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. Computat Social Sci Res Ctr Educ and Network Studie, Hungary.
Swiss Fed Inst Technol, Switzerland; Univ Manchester, England; Univ Manchester, England.
2020 (English)In: Social Networks, ISSN 0378-8733, E-ISSN 1879-2111, Vol. 60, p. 129-145Article in journal (Refereed) Published
Abstract [en]

An important puzzle in social network research is to explain how macro-level structures emerge from micro-level network processes. Explaining the emergence and stability of structural groups in social networks is particularly difficult for two reasons. First, because groups are characterized both by high connectedness within (group cohesion) and lack of connectedness between them (group boundaries). Second, because a large number of theoretical micro-level network processes contribute to their emergence. We argue that traditional social network theories that are concerned with the evolution of positive relations (forces of attraction) are not sufficient to explain the emergence of groups because they lack mechanisms explaining the emergence of group boundaries. Models that additionally account for the evolution of negative ties (forces of repulsion) may be better suited to explain the emergence and stability of groups. We build a theoretical model and illustrate its usefulness by fitting stochastic actor-oriented models (SAOMs) to empirical data of co-evolving networks of friendship and dislike among 479 secondary-school students. The SAOMs include a number of newly developed effects expressing the co-evolution between positive and negative ties. We then simulate networks from the estimated models to explore the micro-macro link. We find that a model that considers forces of attraction and repulsion simultaneously is better at explaining groups in social networks. In the long run, however, the empirically informed simulations generate networks that are too stylized to be realistic, raising further questions about model degeneracy and time heterogeneity of group processes.

Place, publisher, year, edition, pages
ELSEVIER , 2020. Vol. 60, p. 129-145
Keywords [en]
Definition of a group; Positive and negative ties; Structural balance; Stochastic actor-oriented models (SAOMs); Agent-based models
National Category
Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:liu:diva-162915DOI: 10.1016/j.socnet.2019.10.008ISI: 000501619700011OAI: oai:DiVA.org:liu-162915DiVA, id: diva2:1382391
Note

Funding Agencies|Swiss National Science FoundationSwiss National Science Foundation (SNSF) [10001A_169965]; European Research Council(ERC) under the European Unions Horizon 2020 research and innovation programmeEuropean Research Council (ERC) [648693]; Hungarian Research Fund (OTKA)Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA) [K81336]; Lendulet Grant of the Hungarian Academy of Sciences

Available from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-02-05

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