Entropy Aware Message Passing in Graph Neural Networks
<|reference_start|>Entropy Aware Message Passing in Graph Neural Networks: 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.<|reference_end|>
@article{nazari2024entropy,
    title={Entropy Aware Message Passing in Graph Neural Networks},
    author={Philipp Nazari, Oliver Lemke, Davide Guidobene, Artiom Gesp},
    journal={arXiv preprint arXiv:2403.04636},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04636},
    primaryClass={cs.LG}
}