Summary of Rethinking and Accelerating Graph Condensation: a Training-free Approach with Class Partition, by Xinyi Gao et al.
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
by Xinyi Gao, Guanhua Ye, Tong Chen, Wentao Zhang, Junliang Yu, Hongzhi Yin
First submitted to arxiv on: 22 May 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 The paper presents a new approach to condensing large-scale graphs for efficient training of Graph Neural Networks (GNNs). The authors identify issues with existing graph condensation (GC) methods, which rely on complex optimization processes that require significant computing resources and time. To overcome these limitations, the researchers propose Class-partitioned Graph Condensation (CGC), a training-free framework that uses clustering methods to efficiently match node distributions between the original and condensed graphs. CGC also incorporates a pre-defined graph structure to eliminate the need for iterative gradient computations. The authors demonstrate the effectiveness of CGC through extensive experiments on large-scale graph datasets, achieving significant speedups and accuracy improvements compared to state-of-the-art GC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier to train computers to understand big graphs by using a new way to make smaller versions of these graphs. Right now, this process takes a long time and uses a lot of computer power. The researchers found that existing methods are too complicated and slow. To solve this problem, they created a new approach called Class-partitioned Graph Condensation (CGC). CGC is faster and more accurate than other methods because it doesn’t need to use as much computing power or iterate through calculations multiple times. This makes it easier to train computers to understand big graphs quickly and accurately. |
Keywords
» Artificial intelligence » Clustering » Optimization
