Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs

We propose TopGraphVisualizer, a tool to support the discovery of relevant topological patterns in attributed graphs. It relies on a new pattern detection method that crucially needs for sophisticated post processing and visualization. A topological pattern is defined as a set of vertex attributes and topological properties (i.e., properties that characterize the role of a vertex within a graph) that strongly co-vary over the vertices of the graph. For instance, such a pattern in a co-authorship attributed graph where vertices represent authors, edges encode coauthor ship, and vertex attributes reveal the number of publications in several journals, could be "the higher the number of publications in IEEE ICDM, the higher the closeness centrality of the vertex within the graph". Two different ways of navigation through the topological patterns and the related graph data are provided to the end-user. We exploit graph visualization and exploration techniques from the open platform Gephi. As an illustrative scenario, we consider a co-autorship attributed graph built from DBLP digital library and a video has been produced that describe the main possibilities of the TopGraphVisualizer software.