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Social Group Dynamics in Networks

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Adaptive Networks

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Due to the frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. The cohesive groups of people in such networks can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We discuss a new algorithm based on a clique percolation technique, that allows to investigate in detail the time dependence of communities on a large scale and as such, to uncover basic relationships of the statistical features of community evolution. According to the results, the behaviour of smaller collaborative or friendship circles and larger communities, e.g., institutions show significant differences. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.

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Author information

Authors and Affiliations

  1. Statistical and Biological Physics Research Group of HAS, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary

    Gergely Palla & Péter Pollner

  2. Center for Complex Network Research, Department of Physics, Biology and Computer Science, Northeastern University, 02115, Boston, MA, USA

    Albert-László Barabási

  3. Department of Biological Physics, Statistical and Biological Physics Research Group of HAS, Eötvös University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary

    Tamás Vicsek

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  1. Gergely Palla
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  2. Péter Pollner
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  3. Albert-László Barabási
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  4. Tamás Vicsek
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Editors and Affiliations

  1. MPI für Physik Komplexer Systeme, Nöthnitzer Str. 38, Dresden, 01187, Germany

    Thilo Gross

  2. Binghamton University, State University of New York, Binghamton, 13902-6000, U.S.A.

    Hiroki Sayama

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Palla, G., Pollner, P., Barabási, AL., Vicsek, T. (2009). Social Group Dynamics in Networks. In: Gross, T., Sayama, H. (eds) Adaptive Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01284-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-01284-6_2

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