Summary of Computing Approximate Graph Edit Distance Via Optimal Transport, by Qihao Cheng et al.
Computing Approximate Graph Edit Distance via Optimal Transport
by Qihao Cheng, Da Yan, Tianhao Wu, Zhongyi Huang, Qin Zhang
First submitted to arxiv on: 25 Dec 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 The proposed ensemble approach integrates supervised and unsupervised learning-based methods for graph edit distance (GED) approximation. The supervised method, GEDIOT, leverages inverse optimal transport with a learnable Sinkhorn algorithm to generate the vertex coupling matrix. The unsupervised method, GEDGW, models GED computation as a linear combination of optimal transport and its variant, Gromov-Wasserstein discrepancy, for node and edge operations. Extensive experiments demonstrate that the proposed methods significantly outperform existing methods in terms of GED computation performance, edit path generation, and model generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed new ways to calculate the graph edit distance (GED) between two graphs. They used a combination of machine learning techniques, including inverse optimal transport and Sinkhorn algorithms, to create an “editing plan” that shows how one graph can be transformed into another. The proposed methods were tested on many different graphs and showed better results than previous approaches. |
Keywords
» Artificial intelligence » Machine learning » Supervised » Unsupervised
