Subgraph Mining on Directed and Weighted Graphs

  • Stephan Günnemann
  • Thomas Seidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6119)

Abstract

Subgraph mining algorithms aim at the detection of dense clusters in a graph. In recent years many graph clustering methods have been presented. Most of the algorithms focus on undirected or unweighted graphs. In this work, we propose a novel model to determine the interesting subgraphs also for directed and weighted graphs. We use the method of density computation based on influence functions to identify dense regions in the graph. We present different types of interesting subgraphs. In experiments we show the high clustering quality of our GDens algorithm. GDens outperforms competing approaches in terms of quality and runtime.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stephan Günnemann
    • 1
  • Thomas Seidl
    • 1
  1. 1.Data management and data exploration groupRWTH Aachen UniversityGermany

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