Multiple Network Motif Clustering with Genetic Algorithms

  • Clara Pizzuti
  • Annalisa Socievole
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 830)


The definition of community, usually, relies on the concept of edge density. Network motifs, however, have been recognized as fundamental building blocks of networks and, similarly to edges, may give insights for uncovering communities in complex networks. In this work, we propose a novel approach for identifying communities of network motifs. Differently from previous approaches, our method focuses on searching communities where nodes simultaneously participate in several types of motifs. Based on a genetic algorithm, the method finds a number of communities by minimizing the concept of multiple-motifs conductance. Simulations on a real-world network show that the proposed algorithm is able to better capture the real modular structure of the network, outperforming both motifs-based and classic community detection algorithms.


Community detection Network motifs Evolutionary techniques Genetic algorithm 


  1. 1.
    Arenas, A., Fernández, A., Fortunato, S., Gómez, S.: Motif-based communities in complex network. J. Phys. A: Math. Theor. 42(22), 224001 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Benson, A.R., Gleich, D.F., Leskovec, J.: Tensor spectral clustering for partitioning higher-order network structures. In: Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, 30 April–2 May 2015, pp. 118–126 (2015)Google Scholar
  3. 3.
    Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)CrossRefGoogle Scholar
  4. 4.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefevre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, P10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Cover, T.M., Thomas, J.A.: Elements of Inf. Wiley, Theory (1991)Google Scholar
  6. 6.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. In: Proceedings of National Academy of Science. USA 1999, pp. 7821–7826 (2002)Google Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  8. 8.
    Grünwald, P.D., Myung, I.J., Pitt, M.A.: Advances in Minimum Description Length: Theory and Applications. MIT Press, Cambridge (2005)Google Scholar
  9. 9.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)CrossRefzbMATHGoogle Scholar
  10. 10.
    Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 353(298), 824–827 (2002)CrossRefGoogle Scholar
  12. 12.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E69, 026113 (2004)Google Scholar
  13. 13.
    Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proceedings of 3rd Annual Conference on Genetic Algorithms. Morgan Kaufmann Publishers, pp. 2–9 (1989)Google Scholar
  14. 14.
    Pizzuti, C., Socievole, A.: An evolutionary motifs-based algorithm for community detection. In Proceedings of 8th IEEE International Conference on Information, Intelligences, Systems and Applications (IISA 2017) (2017)Google Scholar
  15. 15.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  16. 16.
    Schaeffer, S.E.: Survey: graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)CrossRefzbMATHGoogle Scholar
  17. 17.
    Serrour, B., Arenas, A., Gómez, S.: Detecting communities of triangles in complex networks using spectral optimization. Comput. Commun. 34(5), 629–634 (2011)CrossRefGoogle Scholar
  18. 18.
    Ulanowicz, R.E., Bondavalli, C., Egnotovich, M.S.: Network analysis of trophic dynamics in South Florida ecosystem, FY 97: the Florida Bay ecosystem. Annual Report to the United States Geological Service Biological Resources Division Ref. No.[UMCES] CBL, pp. 98–123 (1998)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR)Rende (CS)Italy

Personalised recommendations