Summary of Exgrg: Explicitly-generated Relation Graph For Self-supervised Representation Learning, by Mahdi Naseri et al.
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning
by Mahdi Naseri, Mahdi Biparva
First submitted to arxiv on: 9 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 presents a novel approach to self-supervised learning (SSL) for graph-structured data. The authors introduce Explicitly Generate a compositional Relation Graph (ExGRG), which generates a relation graph that incorporates prior domain knowledge and online extracted information, unlike conventional augmentation-based methods. The proposed method uses an Expectation-Maximization (EM) perspective to generate the relation graph, guiding the SSL invariance objective and updating model parameters accordingly. Experimental results on various node classification datasets show that ExGRG outperforms state-of-the-art techniques, making it a viable option for graph representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how computers can get better at understanding graphs without needing lots of labels. They developed a new way to do this called ExGRG, which is like a special map that shows relationships between things on the graph. This map helps the computer learn from the data it has, rather than just guessing. The new method does a great job compared to other ways people have tried doing this before, and it could be very useful for many different areas of study. |
Keywords
* Artificial intelligence * Classification * Representation learning * Self supervised
