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Community Structure in Social and Biological Networks

Author

Listed:
  • Michelle Girvan
  • M. E. J. Newman

Abstract

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.

Suggested Citation

  • Michelle Girvan & M. E. J. Newman, 2001. "Community Structure in Social and Biological Networks," Working Papers 01-12-077, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:01-12-077
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    Cited by:

    1. Ding, Liang & Huang, Ziqian & Xiao, Chaowei, 2023. "Are human activities consistent with planning? A big data evaluation of master plan implementation in Changchun," Land Use Policy, Elsevier, vol. 126(C).
    2. Li, Shudong & Jiang, Laiyuan & Wu, Xiaobo & Han, Weihong & Zhao, Dawei & Wang, Zhen, 2021. "A weighted network community detection algorithm based on deep learning," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    3. Yi Shen & Gang Ren & Bin Ran, 2021. "Cascading failure analysis and robustness optimization of metro networks based on coupled map lattices: a case study of Nanjing, China," Transportation, Springer, vol. 48(2), pages 537-553, April.
    4. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    5. Govindaraj, T., 2008. "Characterizing performance in socio-technical systems: A modeling framework in the domain of nuclear power," Omega, Elsevier, vol. 36(1), pages 10-21, February.
    6. Yang, Bo & Li, Xu & Liu, Xiangwei & He, He & Chen, Wei, 2019. "Alternating between consensus and leader selection reveals community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 693-706.

    More about this item

    Keywords

    Networks; community; social networks; biological networks; food web; collaboration; graph theory;
    All these keywords.

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