Incremental frequent subgraph mining on large evolving graphs

Abstract

Frequent subgraph mining is a core graph operation used in many domains. Most existing techniques target static graphs. However, modern applications utilize large evolving graphs. Mining these graphs using existing techniques is infeasible because of the high computational cost. We propose IncGM+, a fast incremental approach for frequent subgraph mining on large evolving graphs. We adapt the notion of 'fringe' to the graph context, that is, the set of subgraphs on the border between frequent and infrequent subgraphs. IncGM+ maintains fringe subgraphs and exploits them to prune the search space. To boost efficiency, IncGM+ stores a number of selected embeddings to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Our results confirm that IncGM+ outperforms existing methods, scales to larger graphs and consumes less memory.