The investigation of community structures in networks is an important issue in many domains and disciplines. There have been considerable recent interest algorithms for finding communities in networks. In this paper we present a method of detecting community structure based on hypergraph model. The hypergraph model maps the relationship in the original data into a hypergraph. A hyperedge represents a relationship among subsets of data and the weight of the hyperedge reflects the strength of this affinity. We assign the density of a hyperedge to its weight. We present and illustrate the results of experiments on the Enron data set. These experiments demonstrate that our approach is applicable and effective. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Rong, Q., Wei, Z., & Bingru, Y. (2007). Community detection in scale-free networks based on hypergraph model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4430 LNCS, pp. 226–231). https://doi.org/10.1007/978-3-540-71549-8_20
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