Summary of Graph Neural Networks Need Cluster-normalize-activate Modules, by Arseny Skryagin et al.
Graph Neural Networks Need Cluster-Normalize-Activate Modules
by Arseny Skryagin, Felix Divo, Mohammad Amin Ali, Devendra Singh Dhami, Kristian Kersting
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to counteract oversmoothing in Graph Neural Networks (GNNs) is proposed, enabling the development of deeper architectures for complex tasks. The plug-and-play Cluster-Normalize-Activate (CNA) module introduces super nodes that are normalized and activated individually, allowing GNNs to search and form clusters in each layer. This technique significantly improves accuracy on node classification and property prediction tasks, reaching 94.18% and 95.75% on Cora and CiteSeer datasets respectively, while also benefiting regression tasks by reducing mean squared error. Furthermore, the proposed method requires fewer learnable parameters compared to competing architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are special kinds of AI models that work with data organized as networks or graphs. Sometimes, these models can get stuck in a rut and not be able to solve complex problems. The new CNA module helps GNNs by creating smaller groups within the network and making sure each group is treated equally. This makes the model better at solving tasks like classifying nodes and predicting properties. The results show that this approach works really well, especially when compared to other existing methods. |
Keywords
» Artificial intelligence » Classification » Regression
