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arXiv · 2208.06322

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

Yirui Liu·Xinghao Qiao·Liying Wang·Jessica Lam
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Abstract

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 unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines.

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Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam. 2022-08-12. EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model. https://arxiv.org/abs/2208.06322

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