Summary of Hypergraph Self-supervised Learning with Sampling-efficient Signals, by Fan Li et al.
Hypergraph Self-supervised Learning with Sampling-efficient Signals
by Fan Li, Xiaoyang Wang, Dawei Cheng, Wenjie Zhang, Ying Zhang, Xuemin Lin
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel self-supervised learning framework for representation learning on hypergraphs is proposed, addressing limitations in existing contrastive methods. The SE-HSSL framework uses three sampling-efficient self-supervised signals: two sampling-free objectives leveraging canonical correlation analysis at the node-level and group-level, and a hierarchical membership-level contrast objective motivated by the cascading overlap relationship in hypergraphs. Experimental results on 7 real-world hypergraphs demonstrate the superiority of this approach over state-of-the-art methods in terms of both effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a new way to learn from data without needing labels, which is important for big graphs that are hard to work with. The method they came up with uses three different signals to help learn good representations. They tested it on 7 real-world hypergraphs and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Representation learning » Self supervised
