Speeding Up GED Verification for Graph Similarity Search

Graph similarity search retrieves from a database all graphs whose edit distance (GED) to a query graph is within a threshold. As GED computation is NP-hard, the existing works adopt the filtering-and-verification paradigm to reduce the number of GED verifications, and they mainly focus on designing filtering techniques while using the now out-dated algorithm A*GED for verification. In this paper, we aim to speed up GED verification, which is orthogonal to the index structures used in the filtering phase. We propose a best-first search algorithm AStar ⁺ -LSa which improves A*GED by (1) reducing memory consumption, (2) tightening lower bound estimation, and (3) improving the time complexity for lower bound computation. We formally show that AStar ⁺ -LSa has a lower space and time complexity than A*GED. We further modify AStar ⁺ -LSa into a depth-first search algorithm to contrast these two search paradigms, and we extend our algorithms for exact GED computation. We conduct extensive empirical studies on real graph datasets, and show that our algorithm AStar ⁺ -LSa outperforms the state-of-the-art algorithms by several orders of magnitude for both GED verification and GED computation.