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Soft Computing

Erschienen in:

01.04.2013 | Original Paper

Fuzzy community structure detection by particle competition and cooperation

verfasst von: Fabricio Breve, Liang Zhao

Erschienen in: Soft Computing | Ausgabe 4/2013

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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.

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Belkin M, Matveeva I, Niyogi P (2004) Regularization and semisupervised learning on large graphs. In: Conference on learning theory. Springer, Berlin, pp 624–638

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 17–24

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 19–26

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 619–628

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

Chapelle O, Schölkopf B, Zien A (eds) (2006) Semi-supervised learning. In: Adaptive computation and machine learning. The MIT Press, Cambridge

Danon L, Díaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 9:P09,008

Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E Stat Phys Plasmas Fluids 72:027,104

Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2007) Prtools4.1, a matlab toolbox for pattern recognition

Fortunato S (2010) Community detection in graphs. Physics Reports 486(3-5):75–174. doi:10.1016/j.physrep.2009.11.002

Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml

Joachims T (2003) Transductive learning via spectral graph partitioning. In: Proceedings of international conference on machine learning. AAAI Press, Menlo Park, pp 290–297

Karypis G, Han EH, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput Archit Lett 32(8):68–75

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

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

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

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

Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Phys Plasmas Fluids 69:026,113

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

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

Reichardt J, Bornholdt S (2004) Detecting fuzzy community structures in complex networks with a potts model. Phys Rev Lett 93(21):218,701

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

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

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

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 321–328

Zhu X (2005) Semi-supervised learning literature survey. Tech. Rep. 1530, Computer Sciences, University of Wisconsin-Madison

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 912–919

Titel: Fuzzy community structure detection by particle competition and cooperation
verfasst von: Fabricio Breve
Liang Zhao
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 4/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0924-3

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