Subgraph mining on directed and weighted graphs

Authors:
Stephan Günnemann;Thomas Seidl
Affiliations:
Data management and data exploration group, RWTH Aachen University, Germany;Data management and data exploration group, RWTH Aachen University, Germany
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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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.