PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Graphlet Kernels for Large Graph Comparison
Nino Shervashidze, S V N Vishwanathan, Tobias Petri, Kurt Mehlhorn and Karsten Borgwardt
AISTATS 2009 2008.


State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting common {\it graphlets}, \ie subgraphs with $k$ nodes where $k \in \{ 3, 4, 5 \}$. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4965
Deposited By:Karsten Borgwardt
Deposited On:24 March 2009