Summary of Graph Structure Refinement with Energy-based Contrastive Learning, by Xianlin Zeng et al.
Graph Structure Refinement with Energy-based Contrastive Learning
by Xianlin Zeng, Yufeng Wang, Yuqi Sun, Guodong Guo, Baochang Zhang, Wenrui Ding
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Graph Neural Networks (GNNs) are a promising tool for analyzing graph-structured data. However, imperfect graph structures can limit GNN performance in practical tasks. To tackle this issue, we propose an unsupervised method that combines generative and discriminative training to learn graph structure and representation. Our Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR, leverages contrastive learning to increase the similarity between positive pairs of node representations while reducing the similarity between negative ones. This refined structure is produced by augmenting and removing edges according to the similarity metrics among node representations. Our experiments demonstrate that ECL-GSR outperforms state-of-the-art models on eight benchmark datasets in node classification, achieving faster training with fewer samples and memories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to improve how computer programs analyze graph-structured data, like social networks or chemical molecules. Graph Neural Networks are already good at this job, but they can be tricked by fake or noisy connections in the graph. To fix this problem, we came up with an innovative approach that combines two techniques: generating and refining the graph structure. Our method, called ECL-GSR, helps GNNs learn better representations of nodes in the graph. We tested it on many datasets and found that our method works really well, even when there’s less data available. This is important because it makes machine learning tasks faster and more efficient. |
Keywords
» Artificial intelligence » Classification » Gnn » Machine learning » Unsupervised
