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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Graph similarity computation based on cross-graph feature fusion and structure-aware attention

Pang Jun1,2
Yan Bingxin1
Lin Xiaoli1,2
Wang Mengxiang3
1. College of Computer Science & Technology, Wuhan University of Science & Technology, Wuhan Hubei 430065, China
2. Hubei Province Key Laboratory of Intelligent Information Processing & Real-time Industrial System, Wuhan Hubei 430065, China
3. China Institute of Standardization, Beijing 100088, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0020

Abstract

Graph Edit Distance (GED) is a commonly used graph similarity metric function whose exact computation is an NP-hard problem. Therefore, recently researchers have proposed numerous graph neural network-based graph similarity computation methods. The existing methods ignore the cross-graph interaction information between two graph nodes during feature extraction and lack the learning of higher-order relationships between nodes in the graph. To address the above problems, a model for graph similarity computation based on cross-graph feature fusion and structure-aware attention is proposed. First, the model proposes a cross-graph node feature learning method, which introduces a cross-graph attention mechanism to extract the cross-graph interaction information of nodes, and effectively fuses the local features of nodes and the cross-graph interaction features; second, the model proposes a structure-aware multi-attention mechanism, which combines the feature information of nodes with the graph structural information to efficiently capture the higher-order relationships among nodes. Experimental results on three public datasets show that the prediction accuracy of the CFSA model is improved by 4.8%, 5.1%, and 15.8%, respectively, compared to the existing models, and has advantages in a large number of performance metrics, which proves the effectiveness and efficiency of CFSA for the GED prediction task.

Foundation Support

国家自然科学基金资助项目(62372342,62372343)
湖北省自然科学基金项目(2024AFB865)
武汉科技大学"十四五"湖北省优势特色学科(群)项目(2023D0301)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0020
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-04-17] Accepted Paper

Cite This Article

庞俊, 闫炳鑫, 林晓丽, 等. 基于跨图特征融合和结构感知注意力的图相似度计算 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0020. (Pang Jun, Yan Bingxin, Lin Xiaoli, et al. Graph similarity computation based on cross-graph feature fusion and structure-aware attention [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0020. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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