Summary of Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks, by Qian Ma et al.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
by Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma
First submitted to arxiv on: 24 Feb 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 Contrastive Learning (GCL) techniques have gained popularity in the graph learning community, driven by the potential to leverage unlabeled data for various downstream tasks. However, current evaluation standards are flawed due to hyper-parameter tuning and reliance on a single task, which can lead to misleading conclusions. This paper identifies these shortcomings and proposes an enhanced framework to accurately evaluate GCL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph learning is getting better at using data without labels! Researchers have been working on ways to make graph contrastive learning (GCL) work well for different tasks. The problem is that the way we test these methods right now is not perfect. We need to find a better way to see how good they are. This paper talks about why our current tests aren’t great and suggests new ideas to make them better. |
