Abstract
This work aims at proposing an original approach based on formal concept analysis (FCA) for community detection in social networks (SN). Firstly, we study FCA methods which partially detect community in social networks. Secondly we propose a GroupNode modularity function whose goal is to improve a partial detection method taking into account all actors of the social network. Our approach is validated through different experiments based on real known social networks in the field and a synthetic benchmark networks. In addition, we adapted the F-measure function in the case of multi-class in order to evaluate the quality of a detected community.
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References
Falzon, L.: Determining groups from the clique structure in large social networks. Social Networks 22(2), 159–172 (2000)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Sciences 104(1), 36 (2007)
Freeman, L.C.: Cliques, galois lattices, and the structure of human social groups. Social Networks 18(3), 173–187 (1996)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag New York, Inc. (1999); Translator-C. Franzke
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)
Lancichinetti, A., Fortunato, S.: Community detection algorithms: A comparative analysis. Physical Review E 80(5), 056117 (2009)
Makino, K., Uno, T.: New algorithms for enumerating all maximal cliques. In: Hagerup, T., Katajainen, J. (eds.) SWAT 2004. LNCS, vol. 3111, pp. 260–272. Springer, Heidelberg (2004)
Missaoui, R.: Analyse de réseaux sociaux par l’analyse formelle de concepts. In: EGC, pp. 3–4 (2013)
Newman, M.E.: Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)
Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: A survey on applications. Expert Systems with Applications (2013)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Information Processing & Management 45(4), 427–437 (2009)
Tummarello, G., Morbidoni, C.: The dbin platform: A complete environment for semanticweb communities. Web Semantics: Science, Services and Agents on the World Wide Web 6(4) (2008)
Van Rijsbergen, C.: Information retrieval. dept. of computer science, university of glasgow (1979), citeseer.ist.psu.edu/vanrijsbergen79information.html
Wille, R.: Restructuring lattice theory: An approach based on hierarchies of concepts. In: Ferré, S., Rudolph, S. (eds.) ICFCA 2009. LNCS, vol. 5548, pp. 314–339. Springer, Heidelberg (2009)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473 (1977)
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Ali, S.S., Bentayeb, F., Missaoui, R., Boussaid, O. (2014). An Efficient Method for Community Detection Based on Formal Concept Analysis. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_7
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DOI: https://doi.org/10.1007/978-3-319-08326-1_7
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