Summary of Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation, by Xinjian Zhao et al.
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
by Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo, Xiangyu Zhao
First submitted to arxiv on: 16 Feb 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 The proposed Adversarial Curriculum Graph Contrastive Learning (ACGCL) framework addresses the challenge of generating precise positive and negative samples in graph representation learning. ACGCL uses pair-wise augmentation to control similarity between samples, alongside subgraph contrastive learning to identify effective patterns. The framework employs an innovative adversarial curriculum training methodology that progressively increases difficulty by focusing on more challenging data. This approach surpasses state-of-the-art baselines on six benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are used to represent complex data in many fields. To learn good representations from graphs, a technique called graph contrastive learning is important. The problem with this technique is that it’s hard to make sure the generated samples are similar or not similar enough to the original data. A new way of doing this called Adversarial Curriculum Graph Contrastive Learning (ACGCL) makes it easier to learn good representations by generating samples with different levels of similarity. This helps the model get better and better at understanding graphs. |
Keywords
* Artificial intelligence * Representation learning
