Graph Neural Networks with a Distribution of Parametrized Graphs

• URL: http://arxiv.org/abs/2310.16401v3
• Date: Sat, 3 Feb 2024 04:45:45 GMT
• Title: Graph Neural Networks with a Distribution of Parametrized Graphs
• Authors: See Hian Lee, Feng Ji, Kelin Xia and Wee Peng Tay
• Abstract summary: We introduce latent variables to parameterize and generate multiple graphs. We obtain the maximum likelihood estimate of the network parameters in an Expectation-Maximization framework.
• Score: 27.40566674759208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and capture additional information previously absent in the observed graph, we introduce latent variables to parameterize and generate multiple graphs. We obtain the maximum likelihood estimate of the network parameters in an Expectation-Maximization (EM) framework based on the multiple graphs. Specifically, we iteratively determine the distribution of the graphs using a Markov Chain Monte Carlo (MCMC) method, incorporating the principles of PAC-Bayesian theory. Numerical experiments demonstrate improvements in performance against baseline models on node classification for heterogeneous graphs and graph regression on chemistry datasets.

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