Summary of Gps: Graph Contrastive Learning Via Multi-scale Augmented Views From Adversarial Pooling, by Wei Ju et al.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
by Wei Ju, Yiyang Gu, Zhengyang Mao, Ziyue Qiao, Yifang Qin, Xiao Luo, Hui Xiong, Ming Zhang
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 research proposes a novel approach called Graph Pooling ContraSt (GPS) for self-supervised graph representation learning. The authors leverage graph pooling to generate multi-scale positive views, which are then used in a joint contrastive learning framework. This approach addresses limitations of existing methods that rely on pre-defined augmentation strategies and may fail to provide sufficient supervision signals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GPS method automatically generates strongly-augmented views with varying emphasis on providing challenging positives and preserving semantics. The authors incorporate both views into the joint contrastive learning framework, which includes similarity learning and consistency learning. This approach is trained adversarially for robustness. |
Keywords
* Artificial intelligence * Representation learning * Self supervised * Semantics
