More Interpretable Graph Similarity Computation Via Maximum Common Subgraph Inference

Graph similarity measurement is a fundamental task in various graph-related applications. However, recent learning-based approaches lack interpretability as they directly transform interaction information between two graphs into a hidden vector, making it difficult to understand how the similarity score is derived. To address this issue, we propose an end-to-end paradigm for graph similarity learning called Similarity Computation via Maximum Common Subgraph Inference (INFMCS), which is more interpretable. Our key insight is that the similarity score has a strong correlation with the Maximum Common Subgraph (MCS). We implicitly infer the MCS to obtain the normalized MCS size, with only the similarity score being used as supervision information during training. To capture more global information, we stack vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. Our entire model is simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and graph-graph regression tasks. Ablation experiments verify the effectiveness of our proposed computation paradigm and other components. Additionally, visualization and statistical analysis of results demonstrate the interpretability of INFMCS.