Summary of Entropy Aware Message Passing in Graph Neural Networks, by Philipp Nazari et al.
Entropy Aware Message Passing in Graph Neural Networks
by Philipp Nazari, Oliver Lemke, Davide Guidobene, Artiom Gesp
First submitted to arxiv on: 7 Mar 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 novel, physics-inspired GNN model introduced in this paper aims to address the issue of oversmoothing in Deep Graph Neural Networks. By integrating an entropy-aware message passing term with existing GNN architectures, the approach performs gradient ascent on entropy during node aggregation, preserving a certain degree of entropy in the embeddings. This is compared against state-of-the-art GNNs across various common datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of oversmoothing in Deep Graph Neural Networks. Scientists have created a new way to make these networks work better by adding an extra step that helps keep some information from getting lost. They tested this new approach with other popular methods and compared how well they all did on different datasets. |
Keywords
* Artificial intelligence * Gnn
