Summary of Discrete Curvature Graph Information Bottleneck, by Xingcheng Fu et al.
Discrete Curvature Graph Information Bottleneck
by Xingcheng Fu, Jian Wang, Yisen Gao, Qingyun Sun, Haonan Yuan, Jianxin Li, Xianxian Li
First submitted to arxiv on: 28 Dec 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 This research paper proposes a novel framework called Discrete Curvature Graph Information Bottleneck (CurvGIB) that optimizes the information transport structure and learns better node representations in graph neural networks (GNNs). Building on recent advancements in graph connectivity and information propagation efficiency, CurvGIB combines geometric and information theory perspectives to learn optimal transport patterns for specific downstream tasks. The framework advances the Variational Information Bottleneck principle by refining the optimal transport structure using Ricci curvature optimization and deducing a tractable objective function via Ricci flow and VIB. Experimental results on various datasets demonstrate the superior effectiveness and interpretability of CurvGIB. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CurvGIB is a new way to help computers understand how information moves through complex networks, like social media or the internet. The method combines two ideas: graph geometry, which looks at the structure of these networks, and information theory, which helps us understand how information flows. CurvGIB optimizes this flow to make it more efficient and accurate. This could be very useful for tasks like predicting friendships on Facebook or recommending products based on customer behavior. |
Keywords
» Artificial intelligence » Objective function » Optimization
