Summary of Igl-bench: Establishing the Comprehensive Benchmark For Imbalanced Graph Learning, by Jiawen Qin et al.
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
by Jiawen Qin, Haonan Yuan, Qingyun Sun, Lyujin Xu, Jiaqi Yuan, Pengfeng Huang, Zhaonan Wang, Xingcheng Fu, Hao Peng, Jianxin Li, Philip S. Yu
First submitted to arxiv on: 14 Jun 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 A medium-difficulty summary of the abstract would be: This paper introduces IGL-Bench, a comprehensive benchmark for imbalanced graph learning. Imbalanced graph data distributions can lead to biased outcomes in conventional graph learning algorithms. The authors propose 16 diverse graph datasets and 24 distinct IGL algorithms, evaluated on node-level and graph-level tasks, including class-imbalance and topology-imbalance. State-of-the-art IGL algorithms are compared for effectiveness, robustness, and efficiency. The results demonstrate the potential benefits of IGL algorithms in various imbalanced conditions. This work aims to provide a unified package for reproducible evaluation and inspire innovative research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new benchmark called IGL-Bench to help improve graph learning when some parts of the data are very imbalanced. Right now, many different algorithms try to solve this problem, but it’s hard to compare them because they use different methods. The authors fixed this by creating 16 different datasets and 24 different algorithms that can all be tested in the same way. They looked at how well each algorithm worked on different types of problems and found that some algorithms do better than others depending on the situation. This helps us understand which algorithms are best for certain kinds of imbalanced data. |
