Summary of Feature Distribution on Graph Topology Mediates the Effect Of Graph Convolution: Homophily Perspective, by Soo Yong Lee et al.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
by Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung Shin
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 how randomly shuffling feature vectors among nodes from the same class affects the performance of Graph Neural Networks (GNNs). Surprisingly, it finds a consistent and significant improvement in GNN performance when feature vectors are shuffled. This challenges our understanding of the relationship between graph topology and features, as previously overlooked. The study proposes a new measure for this dependence, designs a random graph model that controls for confounding variables, develops a theory on how this dependence relates to graph convolution, and empirically analyzes real-world graphs. It concludes that smaller dependence improves GNN-based node classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at what happens when you mix up the features of nodes from the same class in Graph Neural Networks (GNNs). This changes how GNNs learn from the graph structure and feature relationships. The research shows that this “feature shuffle” actually makes GNNs work better! It’s a big deal because scientists didn’t know what was going on before. |
Keywords
* Artificial intelligence * Classification * Gnn
