Summary of Dual-perspective Cross Contrastive Learning in Graph Transformers, by Zelin Yao et al.
Dual-perspective Cross Contrastive Learning in Graph Transformers
by Zelin Yao, Chuang Liu, Xueqi Ma, Mukun Chen, Jia Wu, Xiantao Cai, Bo Du, Wenbin Hu
First submitted to arxiv on: 1 Jun 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 This paper proposes a novel framework for graph contrastive learning (GCL) called dual-perspective cross graph contrastive learning (DC-GCL). The traditional GCL methods utilize single-perspective augmentation, which can lead to limited positive sample diversity. To address this challenge, the authors introduce three modifications: a dual-perspective augmentation strategy, controllable data augmentation, and model-based pruning-based strategies. These enhancements allow for more diverse and reliable training inputs, leading to significant improvements over traditional GCL methods. The proposed framework is evaluated on various benchmarks, demonstrating consistent performance gains across different datasets and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to learn about graphs using something called contrastive learning. Normally, this type of learning only looks at things from one perspective, but the new method looks at things from two perspectives. This makes it better at understanding graphs by giving it more information. The authors also found ways to make the training data more reliable and useful. They tested their new method on many different types of data and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Data augmentation » Pruning
