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A Force-Directed Layout for Community Detection with Automatic Clusterization

  • Conference paper
Simulating Interacting Agents and Social Phenomena

Part of the book series: Agent-Based Social Systems ((ABSS,volume 7))

Abstract

We present a force-directed layout algorithm to detect community ­structure within a network. Our algorithm places nodes in a two-dimensional grid and continuously updates their positions based on two opposing forces: nodes pull connected nodes closer and push non-connected nodes away. To compute the strength of the forces between nodes we make use of two insightful community properties: high-degree nodes contribute more inter-community edges than low-degree nodes and the graph-distance between two nodes is inversely proportional to the probability that they belong to the same community. We present empirical evidence in support of both of these claims. In conjunction, we use a clustering algorithm to monitor and interpret the current community structure. Running our algorithm on well-known social networks, we find that we produce accurate results that avoid some common pitfalls of alternative approaches.

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Notes

  1. 1.

    There are alternative interpretations of community that allow overlapping communities.

  2. 2.

    Results for other algorithms shown in Table 2 are from [14] and [17].

  3. 3.

    In general this is only possible with well-structured modular networks.

References

  1. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111

    Article  Google Scholar 

  2. Davidson R, Harel D (1996) Drawing graphs nicely using simulated annealing. ACM Trans Graph 15(4):301–331

    Article  Google Scholar 

  3. Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E 72:027104

    Article  Google Scholar 

  4. Eades P (1984) A heuristic for graph drawing. Congr Numer 42:149–160

    MathSciNet  Google Scholar 

  5. Fritzke B (1994) Growing cell structures – a self-organizing network for unsupervised and supervised learning. Neural Netw 7(9):1441–1460

    Article  Google Scholar 

  6. Fortunato S, Barthélemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci USA 104:36–41

    Article  Google Scholar 

  7. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Software Pract Exp 21(11):1129–1164

    Article  Google Scholar 

  8. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  9. Gleiser P, Danon L (2003) Community structure in jazz. Adv Complex Syst 6:565

    Article  Google Scholar 

  10. Kamada T, Kawai S (1989) An algorithm for drawing general undirected graphs. Inform Process Lett 31(1):7–15

    Article  MathSciNet  MATH  Google Scholar 

  11. Lusseau D (2003) The emergent properties of a dolphin social network. Proc R Soc Lond B 270(2):186–188

    Article  Google Scholar 

  12. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  13. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133

    Article  Google Scholar 

  14. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582

    Article  Google Scholar 

  15. Noack A (2004) An energy model for visual graph clustering. In: Proc. symposium on graph drawing (GD 2003). LNCS, vol 2912, pp 425–436. Springer

    Google Scholar 

  16. Noack A (2007) Energy models for graph clustering. J Graph Algorithm Appl 11(2):453–480

    Article  MathSciNet  MATH  Google Scholar 

  17. Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithm Appl 10(2):191–218

    Article  MathSciNet  MATH  Google Scholar 

  18. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101(9):2658–2663

    Article  Google Scholar 

  19. Reichardt J, Bornholdt S (2004) Discovering functional communities in dynamical networks. Phys Rev Lett E 93:218710

    Google Scholar 

  20. Tasgin M, Bingol H (2006) Community detection in complex networks using genetic algorithms. ECCS Oxford, UK

    Google Scholar 

  21. Zachary WW (1977) An Information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473

    Google Scholar 

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Author information

Authors and Affiliations

  1. Syracuse University, Syracuse, NY, USA

    Patrick J. McSweeney, Kishan Mehrotra & Jae C. Oh

Authors
  1. Patrick J. McSweeney
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  2. Kishan Mehrotra
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  3. Jae C. Oh
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Corresponding author

Correspondence to Patrick J. McSweeney .

Editor information

Editors and Affiliations

  1. Department of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Keiki Takadama (Associate Professor) (Associate Professor)

  2. Center for Social Complexity, George Mason University, 4400 University Drive, MSN 6B2, Fairfax, Virginia, 22030, USA

    Claudio Cioffi-Revilla (Professor) (Professor)

  3. Head of LISC (Laboratoire d’Ingénierie des Systèmes Complexes), Cemagref 24 avenue des Landais, 50085, 63172, Aubière Cedex 1, France

    Guillaume Deffuant

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McSweeney, P.J., Mehrotra, K., Oh, J.C. (2010). A Force-Directed Layout for Community Detection with Automatic Clusterization. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol 7. Springer, Tokyo. https://doi.org/10.1007/978-4-431-99781-8_4

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  • DOI: https://doi.org/10.1007/978-4-431-99781-8_4

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-99780-1

  • Online ISBN: 978-4-431-99781-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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