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Advances in Knowledge Discovery and Data Mining

Volume 6119 of the series Lecture Notes in Computer Science pp 133-146

Subgraph Mining on Directed and Weighted Graphs

  • Stephan GünnemannAffiliated withData management and data exploration group, RWTH Aachen University
  • , Thomas SeidlAffiliated withData management and data exploration group, RWTH Aachen University

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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.