Year: 2025
Type: article
Abstract: 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 (), 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, 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.
Source: Data Science and Engineering
Authors: Min Pei, Jianke Yu, Chen Chen, Hanchen Wang, Xiaoyang Wang, Ying Zhang
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,390
OpenAlex ID: W4409256207