Summary of Spegcl: Self-supervised Graph Spectrum Contrastive Learning Without Positive Samples, by Yuntao Shou et al.
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
by Yuntao Shou, Xiangyong Cao, Deyu Meng
First submitted to arxiv on: 14 Oct 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 A novel Graph Contrastive Learning (GCL) framework, called SpeGCL, is proposed to address limitations in existing GCL methods. These methods focus on low-frequency information and neglect high-frequency information, which can be leveraged for faster model convergence. The new approach uses a Fourier transform to extract both high- and low-frequency features and applies contrastive learning in the Fourier space to improve node representation. Additionally, SpeGCL relies solely on negative samples to refine graph embeddings, contrary to existing methods that optimize by pulling positive pairs closer and pushing negative pairs farther away. Experimental results demonstrate the superiority of SpeGCL over state-of-the-art GCL methods across unsupervised, transfer, and semi-supervised learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve Graph Contrastive Learning is developed. This approach, called SpeGCL, helps computers better understand graph data by using both high-frequency and low-frequency information. Most existing methods only use the low-frequency information, which can make them slower to learn. SpeGCL solves this problem by looking at both types of information and using a special mathematical technique to combine them. The new approach also only uses negative examples to improve its results, unlike other methods that use positive examples too. This change helps SpeGCL work better on big datasets. |
Keywords
» Artificial intelligence » Semi supervised » Unsupervised
