Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code

Abstract

To perform cluster analysis on graphs we utilize graph kernels, Weisfeiler-Lehman kernel in particular, to transform graphs into a vector representation. Despite good results, these kernels have been criticized in the literature for high dimensionality and high sensitivity, so we propose an efficient subtree distance measure that is subsequently used to enrich the vector representations and enables more sensitive distance measurements. We demonstrate the usefulness in an application, where the graphs represent different source code snapshots, and a cluster analysis of these snapshots provides the lecturer an overview about the overall performance of a group of students.