Summary of A Survey on Graph Condensation, by Hongjia Xu et al.
A Survey on Graph Condensation
by Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, Bu Jiajun
First submitted to arxiv on: 3 Feb 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 The paper presents Graph Condensation (GC), a solution to the challenge of analyzing large-scale graphs efficiently and effectively. GC reduces the scale of large graphs while preserving essential information for downstream tasks. The authors provide a formal definition of GC, establish a taxonomy that categorizes existing methods into three types based on their objectives, and classify formulations into two categories: modifying original graphs or generating synthetic new ones. Additionally, they conduct a comprehensive survey of datasets and evaluation metrics in this field, highlighting challenges and limitations, outlining future directions, and providing concise guidelines for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to make it easier to analyze big networks. Right now, processing large-scale graphs is hard because it takes too many resources and time. Graph condensation (GC) helps solve this problem by shrinking the size of these big graphs while keeping the important information that we need for later tasks. The authors give a clear definition of GC, group different methods into categories based on what they’re trying to do, and show how to make condensed graphs. They also look at the datasets and ways to measure success in this area, pointing out challenges and limitations, suggesting next steps, and giving hints for further research. |
