Summary of Combining Optimal Transport and Embedding-based Approaches For More Expressiveness in Unsupervised Graph Alignment, by Songyang Chen et al.
Combining Optimal Transport and Embedding-Based Approaches for More Expressiveness in Unsupervised Graph Alignment
by Songyang Chen, Yu Liu, Lei Zou, Zexuan Wang, Youfang Lin, Yuxing Chen, Anqun Pan
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach for unsupervised graph alignment, which finds one-to-one node correspondences between attributed graphs by leveraging only their structural features. The authors combine the strengths of two existing methods: one that computes node representations and matches nodes with similar embeddings, and another that reduces the problem to optimal transport (OT) via Gromov-Wasserstein (GW) learning. They improve the OT cost design using feature transformation and propose an embedding-based heuristic inspired by the Weisfeiler-Lehman test. The framework integrates these modules into a model called CombAlign, which refines node alignment progressively. Experimental results demonstrate significant improvements in alignment accuracy compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding matching nodes between two graphs without any labels or training data. It’s like matching people at a party based on how they look and who they know! The researchers combine two ideas from other papers to make their own approach, which works better than the others. They also add some new tricks to help with tricky cases. The result is a program called CombAlign that does a great job of finding matches. This is important because it can help us understand how things are connected and how they change over time. |
Keywords
* Artificial intelligence * Alignment * Embedding * Unsupervised
