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<p>In the rapidly advancing field of graph-based applications, accurate graph similarity computing (GSC) has become increasingly important. However, due to the complexity of graph structures, this task remains a challenge because of the intricate calculations involved. To solve the limitations of existing works, this paper introduces the Interpretable Graph Fusion Model (<Emphasis FontCategory="NonProportional">IGFM</Emphasis>), a novel framework designed to enhance the accuracy and efficiency of graph similarity computation. Specifically, our model can fully utilize graph structure information and comprehensively assess graph similarity at both fine-grained and coarse-grained levels, ultimately achieving more accurate predictions. Experimented extensively across four real-world datasets, <Emphasis FontCategory="NonProportional">IGFM</Emphasis> demonstrates a significant improvement over existing SOTA methods to solve the GSC challenge. In numerous experimental tests, our model shows performance improvements in terms of MSE (Mean Squared Error), ranging from 4.66% to as much as 56.92% compared to the second-best method.</p>

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IGFM: An Enhanced Graph Similarity Computation Method with Fine-Grained Analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月8日

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  • Min Pei,
  • Jianke Yu,
  • Chen Chen,
  • Hanchen Wang,
  • Xiaoyang Wang,
  • Ying Zhang

摘要

In the rapidly advancing field of graph-based applications, accurate graph similarity computing (GSC) has become increasingly important. However, due to the complexity of graph structures, this task remains a challenge because of the intricate calculations involved. To solve the limitations of existing works, this paper introduces the Interpretable Graph Fusion Model (IGFM), a novel framework designed to enhance the accuracy and efficiency of graph similarity computation. Specifically, our model can fully utilize graph structure information and comprehensively assess graph similarity at both fine-grained and coarse-grained levels, ultimately achieving more accurate predictions. Experimented extensively across four real-world datasets, IGFM demonstrates a significant improvement over existing SOTA methods to solve the GSC challenge. In numerous experimental tests, our model shows performance improvements in terms of MSE (Mean Squared Error), ranging from 4.66% to as much as 56.92% compared to the second-best method.