Community-Detection Cellular Automata with Local and Long-Range Connectivity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7495)


We explore a community-detection cellular automata algorithm inspired by human heuristics, based on information diffusion and a non-linear processing phase with a dynamics inspired by human heuris- tics. The main point of the methods is that of furnishing different “views” of the clustering levels from an individual point of view. We apply the method to networks with local connectivity and long-range rewiring.


Graph theory Community Clustering 


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  1. 1.
    Albert, R., Barabasi, A.L.: Rev. Mod. Phys. 74, 47 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Blatt, M., Wiseman, S., Domany, E.: Superparamagnetic clustering of data. Phys. Rev. E 76, 3251–3254 (1996)Google Scholar
  3. 3.
    Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On Finding Graph Clusterings with Maximum Modularity. In: Brandstädt, A., Kratsch, D., Müller, H. (eds.) WG 2007. LNCS, vol. 4769, pp. 121–132. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72, 026132 (2005)Google Scholar
  5. 5.
    Van Dongen, S.: Graph clustering via a discrete uncoupling process. SIAM. J. Matrix Anal. and Appl. 30, 121–141 (2009)CrossRefGoogle Scholar
  6. 6.
    Dorogovtesev, S.N., Mendes, J.F.F.: Evolution of Networks. Oxford University Press, Oxford (2003)CrossRefGoogle Scholar
  7. 7.
    Forster, K.I., Davis, C.: Repetition priming and frequency attenuation. Journ. Exp. Psyc.: Learning Memory and Cognition 10(4) (1984)Google Scholar
  8. 8.
    Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103, 650–669 (1996)CrossRefGoogle Scholar
  9. 9.
    Gigerenzer, G., Goldstein, G.: Models of ecological rationality: The recognition heuristic. Psyc. Rev. 109(1), 75–90 (2002)CrossRefGoogle Scholar
  10. 10.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Lancichinetti, A., Fortunato, S.: Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 056117 (2009)Google Scholar
  12. 12.
    Lieberman, E., Hauert, C., Nowak, M.A.: Evolutionary dynamics on graphs. Nature 433(7023), 312–316 (2005)CrossRefGoogle Scholar
  13. 13.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: Behavioral Ecology and Sociobiology 54, 396–405 (2003)CrossRefGoogle Scholar
  14. 14.
    Massaro, E., Bagnoli, F., Guazzini, A., Lió, P.: Information dynamics algorithm for detecting communities in networks. Communications in Nonlinear Science and Numerical Simulation 17(11), 4294–4303 (2012)zbMATHCrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J.: Detecting community structure in networks. Europ. Phys. J. B 38, 331–330 (2004)Google Scholar
  16. 16.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)Google Scholar
  17. 17.
    Nicosia, V., Bagnoli, F., Latora, V.: Impact of network structure on a model of diffusion and competitive interaction. EPL 94(68009) (2011)Google Scholar
  18. 18.
    Rosvall, M., Bergstrom, C.T.: An information-theoretic framework for resolving community structure in complex networks. PNAS 18(104), 7327–7331 (2007)CrossRefGoogle Scholar
  19. 19.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118 (2008)CrossRefGoogle Scholar
  20. 20.
    Scott, J.: Social Networks Analysis: A Handbook, 2nd edn. Sage, London (2000)Google Scholar
  21. 21.
    Strogatz, S.H.: Nature (London) 410, 268 (2001)CrossRefGoogle Scholar
  22. 22.
    Tulving, E., Schacter, D.L., Stark, H.A.: Priming effects in word fragment completion are independent of recognition memory. Journ. Exp. Psyc.: Learning Memory and Cognition 8(4) (1982)Google Scholar
  23. 23.
    Wasserman, S., Faust, K.: Social Networks Analysis. University Press, Cambridge (1994)Google Scholar
  24. 24.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 409–410 (1998)CrossRefGoogle Scholar
  25. 25.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dept. Energy and CSDCUniversità di FirenzeFirenzeItaly
  2. 2.INFN, sez. FirenzeItaly
  3. 3.Dept. Psychology and CSDCUniversitá di Firenze, and Institute for Informatics and Telematics (IIT), National Research Council (CNR)PisaItaly

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