Summary of Graph Similarity Computation Via Interpretable Neural Node Alignment, by Jingjing Wang et al.
Graph Similarity Computation via Interpretable Neural Node Alignment
by Jingjing Wang, Hongjie Zhu, Haoran Xie, Fu Lee Wang, Xiaoliang Xu, Yuxiang Wang
First submitted to arxiv on: 13 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 A novel neural node alignment model is proposed for interpretable graph similarity computation. The model relaxes the quadratic assignment problem of Graph Edit Distance (GED) to a linear alignment via embedding features in a node embedding space. A differentiable Gumbel-Sinkhorn module is used to unsupervisedly generate an optimal one-to-one node alignment matrix. Experimental results on real-world graph datasets demonstrate that this method outperforms state-of-the-art methods in graph similarity computation and retrieval tasks, achieving up to 16% reduction in Mean Squared Error (MSE) and up to 12% improvement in retrieval evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find similar things in big networks. It’s like finding the same medicine for someone who has a certain disease. There are special ways to measure how much two medicines or people are alike, but these ways can be hard to use. Scientists have been trying to make new ways that are easier and more accurate. This paper shows a new way that uses special computer models to find the best match. It works better than other methods and can help us understand big networks better. |
Keywords
» Artificial intelligence » Alignment » Embedding » Embedding space » Mse
