Summary of Towards Graph Contrastive Learning: a Survey and Beyond, by Wei Ju et al.
Towards Graph Contrastive Learning: A Survey and Beyond
by Wei Ju, Yifan Wang, Yifang Qin, Zhengyang Mao, Zhiping Xiao, Junyu Luo, Junwei Yang, Yiyang Gu, Dongjie Wang, Qingqing Long, Siyu Yi, Xiao Luo, Ming Zhang
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Information Retrieval (cs.IR); 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 The proposed survey aims to provide a comprehensive overview of Graph Contrastive Learning (GCL), a crucial component of self-supervised learning on graphs. By leveraging unlabeled graph data, GCL enables machine learning models to produce informative representations, reducing reliance on expensive labeled data. The survey covers the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization objectives. Additionally, it explores extensions to other aspects of data-efficient graph learning, such as weakly supervised learning, transfer learning, and related scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GCL is a way for machine learning models to learn from unlabeled graphs. This means they can make useful predictions without needing lots of labeled data. The survey explains how GCL works and shows how it’s used in real-life applications like finding new medicines and analyzing DNA. It also talks about the challenges and future directions in this area. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Optimization » Self supervised » Supervised » Transfer learning
