The skew spectrum of graphs

The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We present a new system of invariant graph features which we call the skew spectrum of graphs. The skew spectrum is based on mapping the adjacency matrix of any (weigted, directed, unlabeled) graph to a function on the symmetric group and computing bispectral invariants. The reduced form of the skew spectrum is computable in O(n3 ) time, and experiments show that on several benchmark datasets it can outperform state of the art graph kernels.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Risi Imre Kondor, Karsten M. Borgwardt
• Keywords: Art Graph Kernels, ICML 2008, Invariant Graph Features, Machine Learning, Skew Spectrum