Mining Minimal Contrast Subgraph Patterns

Roger Ming Hieng Ting, James Bailey

Abstract: In this paper, we introduce a new type of contrast pattern, the minimal contrast subgraph. It is able to capture structural differences between any two collections of graphs and can be useful in chemical compound comparison and building graph classification models. However, mining minimal contrast subgraphs is a challenging task, due to the exponentially large search space and graph (sub)isomorphism problems. We present an algorithm which utilises a backtracking tree to first compute the maximal common edge sets and then uses a minimal hypergraph transversal algorithm, to derive the set of minimal contrast subgraphs. An experimental evaluation demonstrates the potential of our technique for finding interesting differences in graph data.