Summary of Scalable Graph Self-supervised Learning, by Ali Saheb Pasand et al.
Scalable Graph Self-Supervised Learning
by Ali Saheb Pasand, Reza Moravej, Mahdi Biparva, Raika Karimi, Ali Ghodsi
First submitted to arxiv on: 14 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 This paper proposes a novel approach to reduce the computational complexity of regularization self-supervised learning (SSL) methods for graphs, specifically focusing on non-contrastive graph SSL. The authors introduce volume-maximization terms to pre-training loss functions and explore dimension sampling as a way to mitigate scalability issues. They provide theoretical insights into the accuracy of loss computations and support their findings with mathematical derivations. Experimental results demonstrate that node or dimension sampling can reduce computation costs without compromising downstream performance, leading to improved performance in node-level graph prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers learn from graphs, which is important because we use graphs all the time, like social media networks and maps. Right now, it takes a long time for computers to learn from really big graphs. To fix this problem, the authors came up with a new way to reduce the amount of work that computers have to do when learning from graphs. They tested their idea on some real-world graph problems and found that it makes things faster without making them worse. |
Keywords
* Artificial intelligence * Regularization * Self supervised
