Advertisement

Soft Computing

, Volume 17, Issue 4, pp 659–673 | Cite as

Fuzzy community structure detection by particle competition and cooperation

  • Fabricio BreveEmail author
  • Liang Zhao
Original Paper

Abstract

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Keywords

Graph-based method Community detection Particle competition and cooperation Overlapping nodes Outliers 

Notes

Acknowledgments

This work was supported by the São Paulo State Research Foundation (FAPESP), the Brazilian National Research Council (CNPq), and the Foundation for the Development of Unesp (Fundunesp).

References

  1. Belkin M, Matveeva I, Niyogi P (2004) Regularization and semisupervised learning on large graphs. In: Conference on learning theory. Springer, Berlin, pp 624–638Google Scholar
  2. Belkin M, Niyogi P, Sindhwani V (2005) On manifold regularization. In: Proceedings of the tenth international workshop on artificial intelligence and statistics (AISTAT 2005). Society for Artificial Intelligence and Statistics, New Jersey, pp 17–24Google Scholar
  3. Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the eighteenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 19–26Google Scholar
  4. Breve FA, Zhao L, Quiles MG (2009) Uncovering overlap community structure in complex networks using particle competition. In: International conference on artificial intelligence and computational intelligence (AICI’09), vol 5855, pp 619–628Google Scholar
  5. Breve FA, Zhao L, Quiles MG, Pedrycz W, Liu J (2012) Particle competition and cooperation in networks for semi-supervised learning. IEEE Trans Knowl Data Eng 24:1686–1698. doi: 10.1109/TKDE.2011.119. http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.119
  6. Chapelle O, Schölkopf B, Zien A (eds) (2006) Semi-supervised learning. In: Adaptive computation and machine learning. The MIT Press, CambridgeGoogle Scholar
  7. Danon L, Díaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 9:P09,008Google Scholar
  8. Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E Stat Phys Plasmas Fluids 72:027,104Google Scholar
  9. Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2007) Prtools4.1, a matlab toolbox for pattern recognitionGoogle Scholar
  10. Fortunato S (2010) Community detection in graphs. Physics Reports 486(3-5):75–174. doi: 10.1016/j.physrep.2009.11.002 Google Scholar
  11. Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
  12. Joachims T (2003) Transductive learning via spectral graph partitioning. In: Proceedings of international conference on machine learning. AAAI Press, Menlo Park, pp 290–297Google Scholar
  13. Karypis G, Han EH, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput Archit Lett 32(8):68–75Google Scholar
  14. Lancichinetti A, Fortunato S (2009a) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E 80:016,118, doi: 10.1103/PhysRevE.80.016118. http://link.aps.org/doi/10.1103/PhysRevE.80.016118
  15. Lancichinetti A, Fortunato S (2009b) Community detection algorithms: a comparative analysis. Phys Rev E 80:056,117, doi: 10.1103/PhysRevE.80.056117. http://link.aps.org/doi/10.1103/PhysRevE.80.056117
  16. Lancichinetti A, Fortunato S, KertTsz J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033,015. http://stacks.iop.org/1367-2630/11/i=3/a=033015
  17. Newman M (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582Google Scholar
  18. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Phys Plasmas Fluids 69:026,113Google Scholar
  19. Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818. doi:http://dx.doi.org/10.1038/nature03607 Google Scholar
  20. Quiles MG, Zhao L, Alonso RL, Romero RAF (2008) Particle competition for complex network community detection. Chaos 18(3):033,107. doi: 10.1063/1.2956982 Google Scholar
  21. Reichardt J, Bornholdt S (2004) Detecting fuzzy community structures in complex networks with a potts model. Phys Rev Lett 93(21):218,701Google Scholar
  22. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473Google Scholar
  23. Zhang S, Wang RS, Zhang XS (2007a) Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Phys A Stat Mech Appl 374:483–490. doi: 10.1016/j.physa.2006.07.023
  24. Zhang S, Wang RS, Zhang XS (2007b) Uncovering fuzzy community structure in complex networks. Phys Rev E 76(4):046103. doi: 10.1103/PhysRevE.76.046103 Google Scholar
  25. Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol 16. MIT Press, Cambridge, pp 321–328Google Scholar
  26. Zhu X (2005) Semi-supervised learning literature survey. Tech. Rep. 1530, Computer Sciences, University of Wisconsin-MadisonGoogle Scholar
  27. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the twentieth international conference on machine learning, pp 912–919Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceInstitute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)São CarlosBrazil
  2. 2.Department of Statistics, Applied Mathematics and Computation (DEMAC)Institute of Geosciences and Exact Sciences (IGCE), São Paulo State University (UNESP)Rio ClaroBrazil

Personalised recommendations