Summary of Improving Graph Neural Networks by Learning Continuous Edge Directions, By Seong Ho Pahng and Sahand Hormoz
Improving Graph Neural Networks by Learning Continuous Edge Directions
by Seong Ho Pahng, Sahand Hormoz
First submitted to arxiv on: 18 Oct 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 This paper proposes a new Graph Neural Network (GNN) architecture called CoED GNN, which addresses the limitation of traditional GNNs in tasks such as node classification. The key innovation is the introduction of fuzzy edge directions that allow for long-range information transmission across graphs. This is achieved through a novel complex-valued Laplacian for directed graphs with fuzzy edges. The proposed framework aggregates neighbor features scaled by learned edge directions and processes messages from in-neighbors, out-neighbors, and self-features separately. CoED GNN is well-suited for graph ensemble data and demonstrates improved performance on both synthetic and real-world datasets compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way for computers to understand relationships between things on the internet or in networks. The problem with current approaches is that they can’t see very far into these networks. The solution is to give directions to the connections between things, allowing information to flow more freely and be processed better. This improves performance when classifying nodes (like people or devices) based on their features. The new approach is especially useful for analyzing multiple versions of the same network, like different social media platforms. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network
