Summary of Gc-bench: An Open and Unified Benchmark For Graph Condensation, by Qingyun Sun et al.
GC-Bench: An Open and Unified Benchmark for Graph Condensation
by Qingyun Sun, Ziying Chen, Beining Yang, Cheng Ji, Xingcheng Fu, Sheng Zhou, Hao Peng, Jianxin Li, Philip S. Yu
First submitted to arxiv on: 30 Jun 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 Graph Condensation Benchmark (GC-Bench) is a comprehensive evaluation framework that analyzes the performance of various graph condensation algorithms in different scenarios. The paper develops GC-Bench to systematically investigate the effectiveness, transferability, and complexity of 12 state-of-the-art graph condensation methods in node-level and graph-level tasks across 12 diverse graph datasets. The authors also provide an easy-to-use library for training and evaluating different GC methods to facilitate reproducible research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are big collections of information that help us understand lots of things. This paper is about a way to make these graphs smaller while keeping the important parts intact. It’s called graph condensation, or GC for short. Researchers have developed many ways to do this, but it’s hard to compare them all because there isn’t one place where they can be tested and compared. To fix this problem, the authors created a special tool called the Graph Condensation Benchmark, or GC-Bench. It helps us see how well each method works in different situations. |
Keywords
» Artificial intelligence » Transferability
