Abstract
We combine bi- and triclustering to analyse data collected from the Russian online social network Vkontakte. Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users’ interests as tags and use them to describe Vkontakte groups. After this social tagging process we can recommend to a particular user relevant groups to join or new friends from interesting groups which have a similar taste. We present some preliminary results and explain how we are going to apply these methods on massive data repositories.
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Latapy, M., Magnien, C., Vecchio, N.D.: Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31–48 (2008)
Liu, X., Murata, T.: Evaluating community structure in bipartite networks. In: Elmagarmid, A.K., Agrawal, D. (eds.) SocialCom/PASSAT, pp. 576–581. IEEE Computer Society (2010)
Opsahl, T.: Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks 34 (2011) ISSN: 0378-8733, http://www.sciencedirect.com/science/article/pii/S0378873311000360 , doi:10.1016/j.socnet.2011.07.001
Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: TRIAS–An Algorithm for Mining Iceberg Tri-Lattices. In: Proceedings of the Sixth International Conference on Data Mining, ICDM 2006, pp. 907–911. IEEE Computer Society, Washington, DC (2006)
Murata, T.: Detecting communities from tripartite networks. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) WWW, pp. 1159–1160. ACM (2010)
Ignatov, D.I., Kuznetsov, S.O., Magizov, R.A., Zhukov, L.E.: From Triconcepts to Triclusters. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 257–264. Springer, Heidelberg (2011)
Roth, C.: Generalized preferential attachment: Towards realistic socio-semantic network models. In: ISWC 4th Intl Semantic Web Conference, Workshop on Semantic Network Analysis, Galway, Ireland. CEUR-WS Series, vol. 171, pp. 29–42 (2005) ISSN 1613-0073
Roth, C., Cointet, J.P.: Social and semantic coevolution in knowledge networks. Social Networks 32, 16–29 (2010)
Yavorsky, R.: Research Challenges of Dynamic Socio-Semantic Networks. In: Ignatov, D., Poelmans, J., Kuznetsov, S. (eds.) CDUD 2011 - Concept Discovery in Unstructured Data. CEUR Workshop proceedings, vol. 757, pp. 119–122 (2011)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations, 1st edn. Springer-Verlag New York, Inc., Secaucus (1999)
Freeman, L.C., White, D.R.: Using galois lattices to represent network data. Sociological Methodology 23, 127–146 (1993)
Freeman, L.C.: Cliques, galois lattices, and the structure of human social groups. Social Networks 18, 173–187 (1996)
Duquenne, V.: Lattice analysis and the representation of handicap associations. Social Networks 18(3), 217–230 (1996)
White, D.R.: Statistical entailments and the galois lattice. Social Networks 18(3), 201–215 (1996)
Mohr, J.W., Duquenne, V.: The Duality of Culture and Practice: Poverty Relief in New York City, 1888-1917. Theory and Society, Special Double Issue on New Directions in Formalization and Historical Analysis 26(2/3), 305–356 (1997)
Roth, C., Obiedkov, S., Kourie, D.G.: Towards Concise Representation for Taxonomies of Epistemic Communities. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds.) CLA 2006. LNCS (LNAI), vol. 4923, pp. 240–255. Springer, Heidelberg (2008)
Ignatov, D.I., Kaminskaya, A.Y., Kuznetsov, S., Magizov, R.A.: Method of Biclusterzation Based on Object and Attribute Closures. In: Proc. of 8th International Conference on Intellectualization of Information Processing (IIP 2011), Cyprus, Paphos, October 17-24, pp. 140–143. MAKS Press (2010) (in Russian)
Vander Wal, T.: Folksonomy Coinage and Definition (2007), http://vanderwal.net/folksonomy.html (accessed on March 12, 2012)
Poelmans, J., Ignatov, D.I., Viaene, S., Dedene, G., Kuznetsov, S.O.: Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research. In: Perner, P. (ed.) ICDM 2012. LNCS (LNAI), vol. 7377, pp. 273–287. Springer, Heidelberg (2012)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with titanic. Data & Knowledge Engineering 42(2), 189–222 (2002)
Kuznetsov, S.O.: On stability of a formal concept. Ann. Math. Artif. Intell. 49(1-4), 101–115 (2007)
Besson, J., Robardet, C., Boulicaut, J.-F.: Mining a New Fault-Tolerant Pattern Type as an Alternative to Formal Concept Discovery. In: Schärfe, H., Hitzler, P., Øhrstrøm, P. (eds.) ICCS 2006. LNCS (LNAI), vol. 4068, pp. 144–157. Springer, Heidelberg (2006)
Zhao, L., Zaki, M.J.: Tricluster: an effective algorithm for mining coherent clusters in 3d microarray data. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, SIGMOD 2005, pp. 694–705. ACM, New York (2005)
Mirkin, B.G., Kramarenko, A.V.: Approximate Bicluster and Tricluster Boxes in the Analysis of Binary Data. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 248–256. Springer, Heidelberg (2011)
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Gnatyshak, D., Ignatov, D.I., Semenov, A., Poelmans, J. (2012). Gaining Insight in Social Networks with Biclustering and Triclustering. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds) Perspectives in Business Informatics Research. BIR 2012. Lecture Notes in Business Information Processing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33281-4_13
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DOI: https://doi.org/10.1007/978-3-642-33281-4_13
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