Summary of What Is Missing in Homophily? Disentangling Graph Homophily For Graph Neural Networks, by Yilun Zheng et al.
What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
by Yilun Zheng, Sitao Luan, Lihui Chen
First submitted to arxiv on: 27 Jun 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 The paper investigates the phenomenon of graph homophily, where connected nodes tend to share similar characteristics. It critiques the widely used homophily metrics and their ability to reflect Graph Neural Network (GNN) performance. The authors propose a new composite metric, Tri-Hom, which combines label, structural, and feature homophily aspects. They demonstrate the superiority of Tri-Hom through synthetic experiments and real-world benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are networks that connect nodes with similar characteristics. This paper studies how well GNNs understand these connections by measuring “similarity” between nodes. The authors show that current methods don’t always work, so they propose a new way to measure this similarity, called Tri-Hom. They test Tri-Hom on real-world data and find it works better than other methods. |
Keywords
* Artificial intelligence * Gnn * Graph neural network
