Summary of Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift, by Jiaqiang Zhang et al.
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
by Jiaqiang Zhang, Songcan Chen
First submitted to arxiv on: 23 Jul 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 Graph Contrastive Learning (GCL) paradigm is an effective method for node representation learning in graphs. The key components include data augmentation and positive-negative pair selection. However, typical GCL data augmentations can result in under-diversity views and sampling bias. To address these issues, the authors propose two global topological augmentations: mining semantic correlation between nodes in the feature space and utilizing algebraic properties of adjacency matrices for eigen-decomposition. Additionally, a prototype-based negative pair selection is designed to filter false negatives. Experimental results on various tasks demonstrate the advantages of this model compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph contrastive learning (GCL) helps computers learn about graphs, like social networks or brain connections. The problem is that current GCL methods can be too narrow and biased. To fix this, researchers propose new ways to change graph data and select what’s negative. They suggest looking at node relationships in a feature space and using algebraic properties of the graph’s structure. This helps create more diverse views and reduces errors. The results show that this new approach performs better than existing methods on various tasks. |
Keywords
» Artificial intelligence » Data augmentation » Representation learning
