Enhanced Graph Similarity Learning via Adaptive Multi-scale Feature Fusion

Graph similarity computation plays a crucial role in a variety of fields such as chemical molecular structure comparison, social network analysis and code clone detection. However, due to inadequate feature representation, existing methods often struggle to cope with complex graph structures, which in turn limits the feature fusion capability and leads to low accuracy of similarity computation. To address these issues, this paper introduces an Adaptive Multi-scale Feature Fusion(AMFF) framework. AMFF firstly enhances feature extraction through a residual graph neural network, which robustly captures key information in complex graph structures. Based on this, a multi-pooled attention network is used to aggregate multi-scale features and accurately extract key node features while minimizing information loss. Finally, the adaptive multi-scale feature fusion mechanism dynamically adjusts the feature fusion weights according to the interactions between nodes and graph embeddings, thus improving the accuracy and sensitivity of similarity computation. Extensive experiments on benchmark datasets including AIDS700nef, LINUX, IMDBMulti, and PTC show that AMFF significantly outperforms existing methods on several metrics. These results confirm the efficiency and robustness of AMFF in graph similarity computation, providing a promising solution for assessing the similarity of complex graph data.