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SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
[article]
2020
arXiv
pre-print
Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph
arXiv:1808.05689v4
fatcat:g5or3q6tanae3jerfjpikohzta