通过图融合实现并行图相似性计算，提升效率与精度
Neural Network Graph Similarity Computation Based on Graph Fusion
- 将两图节点序列融合为大图，用全局注意力交互
- 在图级和节点级均实现更优的相似性度量效果
- 适合需要高效图匹配的科研与工业场景
图相似性学习对于图分类和相似性搜索等任务至关重要，核心挑战在于有效处理图之间的交互。传统方法对每对图分别计算，导致冗余复杂。本文提出一种并行图交互方法——图融合：将图对的节点序列合并为单一大图，利用全局注意力机制进行交互计算，挖掘跨图信息。我们进一步在图级和节点级引入两种创新而简洁的相似性计算算法。在五个公开数据集上的大量实验表明，该模型不仅在图对分类与回归任务中优于主流基线，且在性能与效率上树立了新基准。代码已开源，地址为 https://github.com/LLiRarry/GFM-code.git。
原文摘要 · Abstract (English)
Graph similarity learning, crucial for tasks such as graph classification and similarity search, focuses on measuring the similarity between two graph-structured entities. The core challenge in this field is effectively managing the interactions between graphs. Traditional methods often entail separate, redundant computations for each graph pair, leading to unnecessary complexity. This paper revolutionizes the approach by introducing a parallel graph interaction method called graph fusion. By merging the node sequences of graph pairs into a single large graph, our method leverages a global attention mechanism to facilitate interaction computations and to harvest cross-graph insights. We further assess the similarity between graph pairs at two distinct levels-graph-level and node-level-introducing two innovative, yet straightforward, similarity computation algorithms. Extensive testing across five public datasets shows that our model not only outperforms leading baseline models in graph-to-graph classification and regression tasks but also sets a new benchmark for performance and efficiency. The code for this paper is open-source and available at https://github.com/LLiRarry/GFM-code.git
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