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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 80))

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Abstract

In this paper a new concept is proposed for finding communities in a social network based on a mixed graph theoretic model of a standard and a bipartite graph. Compared to previous methods the introduced algorithm has the advantage of noise-tolerance and is applicable independently of the size of the clusters in the graph. The cluster core-mining method is based on a modified MST algorithm. Clustering incomplete data is done by using bipartite graphs and fuzzy membership functions.

This work was partially supported by the Hungarian Research Fund No.80352.

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Keszler, A., Kiss, A., Sziranyi, T. (2010). Noise Tolerant Community Detection Using a Mixed Graph Model. In: Nguyen, N.T., Zgrzywa, A., Czyżewski, A. (eds) Advances in Multimedia and Network Information System Technologies. Advances in Intelligent and Soft Computing, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14989-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-14989-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14988-7

  • Online ISBN: 978-3-642-14989-4

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