Summary of Disentangled Generative Graph Representation Learning, by Xinyue Hu et al.
Disentangled Generative Graph Representation Learning
by Xinyue Hu, Zhibin Duan, Xinyang Liu, Yuxin Li, Bo Chen, Mingyuan Zhou
First submitted to arxiv on: 24 Aug 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 This paper proposes a novel self-supervised learning framework called DiGGR (Disentangled Generative Graph Representation Learning) to improve the robustness and explainability of generative graph representation learning. By leveraging latent disentangled factors, DiGGR guides graph mask modeling to learn more disentangled representations, enabling end-to-end joint learning. The proposed approach is evaluated on 11 public datasets for two graph learning tasks, consistently outperforming many previous self-supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative graph models are getting better at learning how to represent graphs without needing labeled data. But current approaches have a problem: they don’t do a great job of separating what they’re learning into meaningful parts. This makes it hard to understand why the model is making certain predictions or decisions. The authors of this paper introduce DiGGR, a new way to learn graph representations that does a better job of separating what’s being learned into understandable pieces. They test their approach on many different datasets and show that it works better than other methods. |
Keywords
» Artificial intelligence » Mask » Representation learning » Self supervised
