Summary of Segmn: a Structure-enhanced Graph Matching Network For Graph Similarity Learning, by Wenjun Wang et al.
SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
by Wenjun Wang, Jiacheng Lu, Kejia Chen, Zheng Liu, Shilong Sang
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Structure-Enhanced Graph Matching Network (SEGMN) aims to boost graph similarity computation (GSC) by incorporating adjacent edge representation into node embeddings and perceiving cross-graph structures. This approach outperforms state-of-the-art GSC methods in the GED regression task, achieving up to 25% improvement with a plug-and-play structure perception matching module. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are similar if they have identical or similar structures. However, current graph similarity computation (GSC) methods don’t fully use edge information to improve node representations. This means that cross-graph nodes aren’t matched well. SEGMN solves this by adding edge info and perceiving the whole graph structure. It does this with a dual embedding learning module and a structure perception matching module. This makes GSC better and more accurate. |
Keywords
» Artificial intelligence » Embedding » Regression
