A method of analysis and visualization of structured datasets based on centrality information

Abstract

We present a new method of quantitative graph analysis and visualization based on vertex centrality measures and distance matrices. After generating distance k-graphs and collecting frequency information about their vertex descriptors, we obtain generic, multidimensional representation of a graph, invariant to graph isomorphism. The histograms of vertex centrality measures, organized in a form of B-matrices, allow to capture subtle changes in network structure during its evolution and provide robust tool for graph comparison and classification. We show that different types of B-matrices and their extensions are useful in graph analysis tasks performed on benchmark complex networks from Koblenz and IAM datasets. We compare the results obtained for proposed Bmatrix extensions with performance of other state-of-art graph descriptors showing that our method is superior to others.