Summary of Representation Learning on Heterophilic Graph with Directional Neighborhood Attention, by Qincheng Lu et al.
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
by Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 Graph Attention Network (GAT), a popular Graph Neural Network (GNN) architecture, uses attention mechanisms to learn edge weights and has shown promising performance in various applications. However, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly heterophilic graphs. To address this limitation, we propose the Directional Graph Attention Network (DGAT), which combines feature-based attention with global directional information extracted from the graph topology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GAT is a type of graph neural network that uses an attention mechanism to learn edge weights and has been shown to be very effective in many applications. However, it has some limitations, such as not being able to capture long-range and global graph information. To fix this, we propose the DGAT, which adds more information from the graph to help with this. |
Keywords
* Artificial intelligence * Attention * Gnn * Graph attention network * Graph neural network
