More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference. (arXiv:2208.04580v3 [cs.LG] UPDATED)

Graph similarity measurement, which computes the distance/similarity between
two graphs, arises in various graph-related tasks. Recent learning-based
methods lack interpretability, as they directly transform interaction
information between two graphs into one hidden vector and then map it to
similarity. To cope with this problem, this study proposes a more interpretable
end-to-end paradigm for graph similarity learning, named Similarity Computation
via Maximum Common Subgraph Inference (INFMCS). Our critical insight into
INFMCS is the strong correlation between similarity score and Maximum Common …

arxiv computation graph inference