Summary of The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges, by Sitao Luan et al.
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
by Sitao Luan, Chenqing Hua, Qincheng Lu, Liheng Ma, Lirong Wu, Xinyu Wang, Minkai Xu, Xiao-Wen Chang, Doina Precup, Rex Ying, Stan Z. Li, Jian Tang, Guy Wolf, Stefanie Jegelka
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 This paper challenges the long-held assumption that Graph Neural Networks (GNNs) outperform traditional Neural Networks (NNs) on graph-structured data due to homophily. The authors argue that heterophily, or low homophily, is a more significant factor in explaining GNN’s limitations on node-level tasks. They propose re-examining existing graph models, including graph transformers and their variants, in the context of heterophily across various graph types, such as heterogeneous graphs, temporal graphs, and hypergraphs. The paper highlights the importance of addressing heterophily in graph-related applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research question is whether Graph Neural Networks (GNNs) are really better than other neural networks at working with graph data. People thought that GNNs did well because similar things were connected, but now we know that’s not the whole story. Sometimes, GNNs don’t do as well when there isn’t a strong connection between similar things. The authors want to study this problem and find ways to improve how we use GNNs and other graph models. |
Keywords
* Artificial intelligence * Gnn
