Summary of Multiplex Graph Contrastive Learning with Soft Negatives, by Zhenhao Zhao et al.
Multiplex Graph Contrastive Learning with Soft Negatives
by Zhenhao Zhao, Minhong Zhu, Chen Wang, Sijia Wang, Jiqiang Zhang, Li Chen, Weiran Cai
First submitted to arxiv on: 12 Sep 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 The Graph Contrastive Learning (GCL) approach aims to learn graph representations that preserve consistent information from graph-structured data. While node-level contrasting methods dominate, some efforts have begun exploring consistency across different scales, often losing consistent information and introducing noise. The MUX-GCL framework introduces a novel cross-scale contrastive learning paradigm utilizing multiplex representations as effective patches. This approach minimizes contaminating noises by correcting false negative pairs across scales through positional affinities. Extensive downstream experiments demonstrate that MUX-GCL achieves state-of-the-art results on public datasets, while theoretical analysis confirms the new objective function as a stricter lower bound of mutual information between input features and output embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Contrastive Learning (GCL) is a way to learn important information from graph-structured data. Normally, this type of learning focuses on individual nodes within the graph. However, some researchers have started exploring how to keep consistent information across different parts of the graph. This can be tricky because small mistakes in one part of the graph can affect other parts. The MUX-GCL method is a new way to do this that uses multiple types of representations to learn from the graph. This approach helps remove noise and ensures that important information is preserved. The results show that MUX-GCL performs well on various public datasets. |
Keywords
» Artificial intelligence » Objective function
