Entropy Aware Message Passing in Graph Neural Networks
Philipp Nazari Oliver Lemke Davide Guidobene Artiom Gesp
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.