PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The graphlet spectrum
Risi Kondor, Nino Shervashidze and Karsten Borgwardt
In: The 26th International Conference on Machine Learning (ICML 2009), 14-18 June 2009, Montreal, Canada.


Current graph kernels suffer from two limitations: graph kernels based on counting particular types of subgraphs ignore the relative position of these subgraphs to each other, while graph kernels based on algebraic methods are limited to graphs without node labels. In this paper we present the graphlet spectrum, a system of graph invariants derived by means of group representation theory that capture information about the number as well as the position of labeled subgraphs in a given graph. In our experimental evaluation the graphlet spectrum outperforms state-of-the-art graph kernels.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6390
Deposited By:Nino Shervashidze
Deposited On:08 March 2010