Keszler, Anita and Szirányi, Tamás and Tuza, Zsolt (2013) Dense subgraph mining with a mixed graph model. PATTERN RECOGNITION LETTERS, 34 (11). pp. 1252-1262. ISSN 0167-8655 10.1016/j.patrec.2013.03.035

	Text Keszler_1252_2150997_z.pdf Restricted to Registered users only Download (3MB) | Request a copy
Preview	Text Keszler_1252_2150997_ny.pdf - Submitted Version Download (1MB) | Preview

Abstract

In this paper we introduce a graph clustering method based on dense bipartite subgraph mining. The method applies a mixed graph model (both standard and bipartite) in a three-phase algorithm. First a seed mining method is applied to find seeds of clusters, the second phase consists of refining the seeds, and in the third phase vertices outside the seeds are clustered. The method is able to detect overlapping clusters, can handle outliers and applicable without restrictions on the degrees of vertices or the size of the clusters. The running time of the method is polynomial. A theoretical result is introduced on density bounds of bipartite subgraphs with size and local density conditions. Test results on artificial datasets and social interaction graphs are also presented.

Update Item