Summary of Rethinking Structure Learning For Graph Neural Networks, by Yilun Zheng et al.
Rethinking Structure Learning For Graph Neural Networks
by Yilun Zheng, Zhuofan Zhang, Ziming Wang, Xiang Li, Sitao Luan, Xiaojiang Peng, Lihui Chen
First submitted to arxiv on: 12 Nov 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 proposed Graph Structure Learning (GSL) framework for Graph Neural Networks (GNNs) aims to address issues like heterophily, over-squashing, and noisy structures. While GSL is thought to improve GNN performance, it often leads to longer training times and more hyperparameter tuning. This paper critically examines the effectiveness of GSL in GNNs by proposing a new GSL framework with three steps: base construction, new structure construction, and view fusion. The authors analyze the mutual information (MI) between node representations derived from original and newly constructed topologies and find that there is no MI gain compared to the original GSL bases. Ablation experiments reveal that it is the pretrained GSL bases that enhance GNN performance, rather than GSL itself. This finding encourages a rethinking of essential components in GNN design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to answer an important question: does Graph Structure Learning (GSL) really help Graph Neural Networks (GNNs)? To find out, they come up with a new way to do GSL that involves three steps. They also look at how well the new structure works compared to the old one and find that there’s no big difference. Then, they try different things without using GSL and see if GNNs still work well. Surprisingly, it turns out that it’s not GSL that makes GNNs better, but instead it’s what you do with the results of GSL. |
Keywords
» Artificial intelligence » Gnn » Hyperparameter
