Summary of Gcondenser: Benchmarking Graph Condensation, by Yilun Liu and Ruihong Qiu and Zi Huang
GCondenser: Benchmarking Graph Condensation
by Yilun Liu, Ruihong Qiu, Zi Huang
First submitted to arxiv on: 23 May 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 paper proposes a benchmark, GCondenser, to evaluate and compare graph condensation (GC) methods for large-scale graphs. GC compresses the original graph into a smaller one, facilitating efficient training of graph representation models. Despite recent advances in GC methods, there is a lack of comprehensive evaluations across different approaches. The proposed benchmark includes standardized procedures for condensation, validation, and evaluation, enabling comparisons between existing methods and extensions to new datasets. A performance study demonstrates the effectiveness of various GC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called GCondenser that helps compare how well different ways of shrinking big graphs work. Graphs are like super-powerful computers, but they can be too slow if they’re really big. Shrinking them makes training faster and more efficient. Lots of smart people have come up with different methods to do this, but nobody has compared all the different ways yet. This paper fixes that by making a special set of rules for testing these methods, so we can see which ones work best. It’s like comparing superpowers! |
