Comparison of large graphs using distance information

Abstract

We present a new framework for analysis and visualization of large complex networks based on structural information retrieved from their distance k-graphs and B-matrices. The construction of B-matrices for graphs with more than 1 million edges requires massive BFS computations and is facilitated using Cassovary - an open-source in-memory graph processing engine. The approach described in this paper enables efficient generation of expressive, multi-dimensional descriptors useful in graph embedding and graph mining tasks. In experimental section, we present how the developed tools helped in the analysis of real-world graphs from Stanford Large Network Dataset Collection.