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

Pei, M; Yu, J; Chen, C; Wang, H; Wang, X; Zhang, Y

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

Pei, M Yu, J

Chen, C Wang, H Wang, X Zhang, Y

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Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: Data Science and Engineering, 2025
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Pei, M
dc.contributor.author	Yu, J https://orcid.org/0000-0002-2032-7727
dc.contributor.author	Chen, C
dc.contributor.author	Wang, H
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638
dc.date.accessioned	2025-07-15T06:11:40Z
dc.date.available	2025-07-15T06:11:40Z
dc.date.issued	2025-01-01
dc.identifier.citation	Data Science and Engineering, 2025
dc.identifier.issn	2364-1185
dc.identifier.issn	2364-1541
dc.identifier.uri	http://hdl.handle.net/10453/188337
dc.description.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 (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.
dc.language	English
dc.publisher	SPRINGERNATURE
dc.relation.ispartof	Data Science and Engineering
dc.relation.isbasedon	10.1007/s41019-024-00278-3
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4605 Data management and data science
dc.title	IGFM: An Enhanced Graph Similarity Computation Method with Fine-Grained Analysis
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-15T06:11:38Z
pubs.publication-status	Published

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 (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.

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http://hdl.handle.net/10453/188337