Liu, Yirui, Qiao, Xinghao 
ORCID: 0000-0002-6546-6595, Wang, Liying and Lam, Jessica
(2023)
EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model.
Proceedings of Machine Learning Research, 206.
pp. 2132-2146.
ISSN 1938-7228
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Text (EEGNN. Edge Enhanced Graph Neural Network with a Bayesian)
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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.
| Item Type: | Article |
|---|---|
| Official URL: | https://proceedings.mlr.press/v206/ |
| Additional Information: | © 2023 The Author(s) |
| Divisions: | Statistics |
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Date Deposited: | 04 Aug 2023 15:39 |
| Last Modified: | 31 Oct 2025 06:48 |
| URI: | http://eprints.lse.ac.uk/id/eprint/119918 |
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