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Graph Neural Aggregation-diffusion with Metastability
Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied.
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Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
This paper presents a novel study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs).
Improved Naive Bayes with Mislabeled Data
Labeling mistakes are frequently encountered in real-world applications.
Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective
Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model.
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