• Corpus ID: 257206180

EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

@inproceedings{Liu2022EEGNNEE,
  title={EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model},
  author={Yirui Liu and Xinghao Qiao and Liying Wang and Jessica Lam},
  year={2022}
}
Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of {over-smoothing} and {under-reaching} to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, {mis-simplification}, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be… 
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