Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

• URL: http://arxiv.org/abs/2011.15039v2
• Date: Tue, 1 Dec 2020 02:05:29 GMT
• Title: Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
• Authors: Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang
• Abstract summary: This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy.
• Score: 108.49014907941891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge. This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. To this end, our method can be readily integrated into A* procedure in a dynamic fashion, as well as significantly reduce the computational burden with a learned heuristic. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy. To our best knowledge, this work is also the first deep learning-based GED method for recovering the edit path.

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