Summary of Bi-directional Multi-scale Graph Dataset Condensation Via Information Bottleneck, by Xingcheng Fu et al.
Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
by Xingcheng Fu, Yisen Gao, Beining Yang, Yuxuan Wu, Haodong Qian, Qingyun Sun, Xianxian Li
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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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 The novel Bi-directional Multi-Scale Graph Dataset Condensation (BiMSGC) framework, based on mutual information theory, effectively condenses multiple scale graphs while preserving maximum utility information. This approach addresses scaling down degradation and scaling up collapse problems in existing efficient works for multi-scale graph dataset condensation. The proposed method estimates an optimal “meso-scale” and achieves stable and consistent “bi-directional” condensation learning by optimizing graph eigenbasis matching with information bottleneck on other scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make models work better on devices with different computing power. It’s about compressing big graphs into smaller ones, while keeping the important information. The method works by finding the right “middle scale” and then using that to condense the rest of the graph. This helps solve problems that previous methods had when trying to do this. |
