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Subgraph similarity search, one of the core problems in graph search,concerns whether a target graph approximately contains a query graph. Theproblem is recently touched by neural methods. However, current neural methodsdo not consider pruning the target graph, though pruning is criticallyimportant in traditional calculations of subgraph similarities. One obstacle toapplying pruning in neural methods is {the discrete property of pruning}. Inthis work, we convert graph pruning to a problem of node relabeling and thenrelax it to a differentiable problem. Based on this idea, we further design anovel neural network to approximate a type of subgraph distance: the subgraphedit distance (SED). {In particular, we construct the pruning component using aneural structure, and the entire model can be optimized end-to-end.} In thedesign of the model, we propose an attention mechanism to leverage theinformation about the query graph and guide the pruning of the target graph.Moreover, we develop a multi-head pruning strategy such that the model canbetter explore multiple ways of pruning the target graph. The proposed modelestablishes new state-of-the-art results across seven benchmark datasets.Extensive analysis of the model indicates that the proposed model canreasonably prune the target graph for SED computation. The implementation ofour algorithm is released at our Github repo:https://github.com/tufts-ml/Prune4SED.

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning