EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model

Yirui Liu, Xinghao Qiao, Liying Wang and Jessica Lam

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

JEL-codes: C1 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2023-04-25
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in Proceedings of Machine Learning Research, 25, April, 2023, 206, pp. 2132-2146. ISSN: 1938-7228

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:119918

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