Abstract
Social communities extraction and their dynamics are one of the most important problems in today’s social network analysis. During last few years, many researchers have proposed their own methods for group discovery in social networks. However, almost none of them have noticed that modern social networks are much more complex than few years ago. Due to vast amount of different data about various user activities available in IT systems, it is possible to distinguish the new class of social networks called multi-layered social network. For that reason, the new approach to community detection in the multi-layered social network, which utilizes multi-layered edge clustering coefficient is proposed in the paper.
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References
Agarwal, N., Galan, M., Liu, H., Subramanya, S.: WisColl: Collective Wisdom based Blog Clustering. Information Sciences 180(1), 39–61 (2010)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech., 10008 (2008)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. the National Academy of Sciences 99(12), 7821–7826 (2002)
Kazienko, P., Bródka, P., Musial, K., Gaworecki, J.: Multi-layered Social Network Creation Based on Bibliographic Data. In: The Second IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, August 20-22, pp. 407–412. IEEE Computer Society Press, USA (2010)
Moody, J., White, D.R.: Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups. American Sociological Review 68(1), 103–127 (2003)
Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446, 664–667 (2007)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. PNAS 101, 2658–2663 (2004)
Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010)
Traud, A.L., Kelsic, E.D., Mucha, P.J., Porter, M.A.: Community structure in online collegiate social networks, eprint arXiv:0809.0690 (2009)
Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as spectroscopy: Automated discovery of community structure within organizations. In: Communities and Technologies, pp. 81–96. Kluwer, B.V., Deventer (2003)
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Bródka, P., Filipowski, T., Kazienko, P. (2013). An Introduction to Community Detection in Multi-layered Social Network. In: Lytras, M.D., Ruan, D., Tennyson, R.D., Ordonez De Pablos, P., García Peñalvo, F.J., Rusu, L. (eds) Information Systems, E-learning, and Knowledge Management Research. WSKS 2011. Communications in Computer and Information Science, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35879-1_23
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DOI: https://doi.org/10.1007/978-3-642-35879-1_23
Publisher Name: Springer, Berlin, Heidelberg
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