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Dense subgraph mining with a mixed graph model
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DOI：10.1016/j.patrec.2013.03.035.png)
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摘要
En 中文
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. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Graph clustering
Mixed graph model
Dense subgraph mining
Cluster seed mining
Social graphs
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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