Information-geometric graph indexing from bags of partial node coverages

Abstract

In a previous work we have uncovered some of the most informative spectral features (Commute Times, Fiedler eigenvector, Perron-Frobenius eigenvector and Node Centrality) for graph discrimination. In this paper we propose a method which exploits information geometry (manifolds and geodesics) to characterize graphlets with covariance matrices involving the latter features. Once we have the vectorized covariance matrices in the tangent space each graph is characterized by a population of vectors in such space. Then we exploit bypass information-theoretic measures for estimating the dissimilarities between populations of vectors. We test this measure in a very challenging database (GatorBait). © 2011 Springer-Verlag Berlin Heidelberg.