Online Structural Graph Clustering Using Frequent Subgraph Mining

The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or the size of the maximum common subgraph. This can serve to structure the graph space and to improve the understanding of the data. In this paper, we present a novel method for structural graph clustering, i.e. graph clustering without generating features or decomposing graphs into parts. In contrast to many related approaches, the method does not rely on computationally expensive maximum common subgraph (MCS) operations or variants thereof, but on frequent subgraph mining. More specifically, our problem formulation takes advantage of the frequent subgraph miner gSpan (that performs well on many practical problems) without effectively generating thousands of subgraphs in the process. In the proposed clustering approach, clusters encompass all graphs that share a sufficiently large common subgraph. The size of t...

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: Madeleine Seeland, Tobias Girschick, Fabian Buchwald, Stefan Kramer
• Keywords: Common Subgraph, Data Mining, Frequent Subgraph, Maximum Common Subgraph, PKDD 2010