Summary of Automated Loss Function Search For Class-imbalanced Node Classification, by Xinyu Guo et al.
Automated Loss function Search for Class-imbalanced Node Classification
by Xinyu Guo, Kai Wu, Xiaoyu Zhang, Jing Liu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
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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 Medium Difficulty summary: The paper addresses class-imbalanced node classification tasks prevalent in real-world scenarios, where learning high-quality node representations is challenging due to uneven node distribution. Engineering loss functions has shown promise, but relies on human expertise and lacks adaptability. To tackle this challenge, the authors introduce a high-performance, flexible, and generalizable automated loss function search framework that significantly improves performance compared to state-of-the-art methods across 15 graph neural network combinations and datasets. The framework’s transferability is also observed to be influenced by homophily in graph-structured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper tries to solve a big problem in computer science where some things are harder to find than others. When we try to use computers to understand how things are connected, it gets tricky because some connections are much more important than others. The researchers came up with a new way to make the computer better at understanding these connections by using special math problems. They tested this new method and found that it worked really well on many different types of data. This is important because it could help us use computers to solve even more complex problems. |
Keywords
» Artificial intelligence » Classification » Graph neural network » Loss function » Transferability
