Summary of Higher Order Graph Attention Probabilistic Walk Networks, by Thomas Bailie et al.
Higher Order Graph Attention Probabilistic Walk Networks
by Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli
First submitted to arxiv on: 18 Nov 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 A novel Message Passing Neural Network (MPNN) module is proposed to address limitations in current graph neural networks. The Higher Order Graphical Attention (HoGA) module leverages feature-vector diversity to reconstruct higher-order relationships between nodes, effectively capturing long-distance dependencies. This approach can be applied to existing attention-based models and has been shown to lead to significant accuracy improvements on benchmark node classification datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are used to represent complex relationships between nodes or variables. Message Passing Neural Networks (MPNNs) have become popular for analyzing these graph structures, but most methods only consider local information within a small neighborhood of each node. A new approach called Higher Order Graphical Attention (HoGA) is designed to capture longer-distance dependencies and improve the performance of MPNNs. |
Keywords
» Artificial intelligence » Attention » Classification » Neural network
