Summary of Redesigning Graph Filter-based Gnns to Relax the Homophily Assumption, by Samuel Rey et al.
Redesigning graph filter-based GNNs to relax the homophily assumption
by Samuel Rey, Madeline Navarro, Victor M. Tenorio, Santiago Segarra, Antonio G. Marques
First submitted to arxiv on: 13 Sep 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 proposes an architecture for graph neural networks (GNNs) that can learn from both homophilic and heterophilic data. The authors critique existing GNNs for relying on the assumption of homophily, which is not always present in real-world datasets. They introduce a new convolutional layer that enhances expressive capacity and prevents oversmoothing. The proposed architecture outperforms state-of-the-art baselines in both homophilic and heterophilic datasets, demonstrating its potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with graph neural networks (GNNs) that don’t work well when the data is not organized in a special way. Right now, GNNs are good at learning from certain types of data, but they can get stuck if the data doesn’t fit their assumptions. The authors suggest a new way to build GNNs that lets them learn from different kinds of data and do better overall. |
