Neural Graph Navigation for Intelligent Subgraph Matching
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914167337779200 |
|---|---|
| author | Ying, Yuchen Dai, Yiyang Li, Wenda Huang, Wenjie Wang, Rui Zheng, Tongya Wang, Yu Yuan, Hanyang Song, Mingli |
| author_facet | Ying, Yuchen Dai, Yiyang Li, Wenda Huang, Wenjie Wang, Rui Zheng, Tongya Wang, Yu Yuan, Hanyang Song, Mingli |
| contents | Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively matches the query graph against the candidate subgraphs of the data graph. However, the lack of awareness of subgraph structural patterns leads to a costly brute-force enumeration, thereby critically motivating the need for intelligent navigation in subgraph matching. To address this challenge, we propose Neural Graph Navigation (NeuGN), a neuro-heuristic framework that transforms brute-force enumeration into neural-guided search by integrating neural navigation mechanisms into the core enumeration process. By preserving heuristic-based completeness guarantees while incorporating neural intelligence, NeuGN significantly reduces the \textit{First Match Steps} by up to 98.2\% compared to state-of-the-art methods across six real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Graph Navigation for Intelligent Subgraph Matching Ying, Yuchen Dai, Yiyang Li, Wenda Huang, Wenjie Wang, Rui Zheng, Tongya Wang, Yu Yuan, Hanyang Song, Mingli Artificial Intelligence Machine Learning Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively matches the query graph against the candidate subgraphs of the data graph. However, the lack of awareness of subgraph structural patterns leads to a costly brute-force enumeration, thereby critically motivating the need for intelligent navigation in subgraph matching. To address this challenge, we propose Neural Graph Navigation (NeuGN), a neuro-heuristic framework that transforms brute-force enumeration into neural-guided search by integrating neural navigation mechanisms into the core enumeration process. By preserving heuristic-based completeness guarantees while incorporating neural intelligence, NeuGN significantly reduces the \textit{First Match Steps} by up to 98.2\% compared to state-of-the-art methods across six real-world datasets. |
| title | Neural Graph Navigation for Intelligent Subgraph Matching |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.17939 |