Summary of Shedding Light on Problems with Hyperbolic Graph Learning, by Isay Katsman et al.
Shedding Light on Problems with Hyperbolic Graph Learning
by Isay Katsman, Anna Gilbert
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 challenges the claim that hyperbolic representation learning models outperform their Euclidean counterparts on graph tasks. The authors argue that when properly trained, simple Euclidean models with comparable numbers of parameters perform similarly or better than hyperbolic models on various graph datasets, including those previously deemed “hyperbolic”. To explain this phenomenon, they analyze the field of hyperbolic graph representation learning and identify three key issues: the lack of diligent baselines, faulty modelling assumptions, and misleading metrics. The authors propose a parametric family of benchmark datasets to assess the applicability of (hyperbolic) graph neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper questions whether hyperbolic representation learning models are really better for graph tasks. It found that simple Euclidean models can perform just as well or even better than these specialized models on certain types of graphs. To understand why this is, the researchers looked at the field of hyperbolic graph representation learning and discovered some problems with how people have been doing things. They propose a new way to test these models to see if they really work. |
Keywords
» Artificial intelligence » Representation learning
