Entropy Aware Message Passing in Graph Neural Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909131466604544 |
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| author | Nazari, Philipp Lemke, Oliver Guidobene, Davide Gesp, Artiom |
| author_facet | Nazari, Philipp Lemke, Oliver Guidobene, Davide Gesp, Artiom |
| contents | Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an entropy-aware message passing term. This term performs gradient ascent on the entropy during node aggregation, thereby preserving a certain degree of entropy in the embeddings. We conduct a comparative analysis of our model against state-of-the-art GNNs across various common datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04636 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entropy Aware Message Passing in Graph Neural Networks Nazari, Philipp Lemke, Oliver Guidobene, Davide Gesp, Artiom Machine Learning I.2.6; I.5.1 Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an entropy-aware message passing term. This term performs gradient ascent on the entropy during node aggregation, thereby preserving a certain degree of entropy in the embeddings. We conduct a comparative analysis of our model against state-of-the-art GNNs across various common datasets. |
| title | Entropy Aware Message Passing in Graph Neural Networks |
| topic | Machine Learning I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2403.04636 |