TY - GEN
T1 - Subgraph mining on directed and weighted graphs
AU - Günnemann, Stephan
AU - Seid, Thomas
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79956303545&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13672-6_14
DO - 10.1007/978-3-642-13672-6_14
M3 - Conference contribution
AN - SCOPUS:79956303545
SN - 3642136710
SN - 9783642136719
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 146
BT - Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
T2 - 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Y2 - 21 June 2010 through 24 June 2010
ER -