Summary of Harnessing Collective Structure Knowledge in Data Augmentation For Graph Neural Networks, by Rongrong Ma et al.
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
by Rongrong Ma, Guansong Pang, Ling Chen
First submitted to arxiv on: 17 May 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 proposed collective structure knowledge-augmented graph neural network (CoS-GNN) addresses the limitation of traditional message passing neural networks by incorporating a diverse set of node- and graph-level structure features. This novel approach improves structural knowledge modeling in both node and graph levels, leading to enhanced graph representations that outperform state-of-the-art models in various tasks, including graph classification, anomaly detection, and out-of-distribution generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph neural networks (GNNs) have achieved great success, but they often ignore valuable structure information. To tackle this issue, researchers introduced data augmentation methods focused on individual features. However, these methods are challenging to scale up with more features. The CoS-GNN solves this problem by introducing a new message passing method that combines node features, attributes, and diverse structure features in augmented graphs. This approach leads to improved graph representations and better performance in tasks like classification, anomaly detection, and out-of-distribution generalization. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Data augmentation » Generalization » Gnn » Graph neural network
