SSGTN: A Graph Similarity Calculation Method Combining Subgraph and Global Features - Xu, Huang (Computers and Artificial Intelligence, 2025)
Graph similarity computation is one of the fundamental challenges in graph data mining, with critical applications in chemical molecule analysis and social network analysis. Traditional methods, such as graph edit distance and maximum common subgraph, are NP-hard and computationally expensive. Although neural network-based approaches have improved efficiency and accuracy, existing methods still exhibit limitations: they often focus excessively on global features while neglecting local substructures, or vice versa, and fail to evaluate the relative importance of node-level, subgraph level, and global features. To address these issues, this paper proposes Similarity Subgraph Global Tensor Network(SSGTN), a novel graph similarity computation framework that integrates subgraph and global features. Specifically, SSGTN employs graph convolutional networks to extract subgraph features and graph attention mechanisms to capture global representations. A Subgraph Global Tensor Network is then designed to dynamically fuse pooled subgraph features with global features, thereby jointly optimizing local and global characterizations. Finally, all features are aggregated to generate the graph similarity score. Experiments on real-world datasets demonstrate that SSGTN outperforms baseline models across three evaluation metrics, confirming its efficiency and accuracy.