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A model for evolution of overlapping community networks

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  • Karan, Rituraj
  • Biswal, Bibhu

Abstract

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

Suggested Citation

  • Karan, Rituraj & Biswal, Bibhu, 2017. "A model for evolution of overlapping community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 380-390.
    • Handle: RePEc:eee:phsmap:v:474:y:2017:i:c:p:380-390
    DOI: 10.1016/j.physa.2017.01.083
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
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    5. Emily M. Jin & Michelle Girvan & M. E. J. Newman, 2001. "The Structure of Growing Social Networks," Working Papers 01-06-032, Santa Fe Institute.
    6. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
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    Cited by:

    1. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    2. Mishra, Nagender & Karan, Rituraj & Biswal, Bibhu & Singh, Harinder P., 2018. "A model for the evolution of the neuronal network in kindled brain slices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 444-453.
    3. Jadhav, Akshay & Orr, Stuart & Malik, Mohsin, 2019. "The role of supply chain orientation in achieving supply chain sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 112-125.

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