Dense subgraph mining with a mixed graph model

Anita Keszler, T. Szirányi, Z. Tuza

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1252-1262
Number of pages11
JournalPattern Recognition Letters
Volume34
Issue number11
DOIs
Publication statusPublished - 2013

Fingerprint

Seed
Refining
Polynomials

Keywords

  • Cluster seed mining
  • Dense subgraph mining
  • Graph clustering
  • Mixed graph model
  • Social graphs

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Dense subgraph mining with a mixed graph model. / Keszler, Anita; Szirányi, T.; Tuza, Z.

In: Pattern Recognition Letters, Vol. 34, No. 11, 2013, p. 1252-1262.

Research output: Contribution to journalArticle

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