A Survey on Bayesian Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) is an important branch of deep learning in graph structure. As a model that can reveal deep topological information, GNNs has been widely used in various learning tasks, including physical system, protein interface prediction, disease classification, learning molecular fingerprints and so on. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is to say, there is a lot of uncertainty associated with the underlying graph structure. The method of modeling uncertainty is to use Bayesian framework, in which graph is regarded as random variable. Introducing Bayesian framework into graph-based model, especially for semi-supervised node classification, has been shown that it can produce higher classification accuracy. In this paper, some GNNs models and Bayesian neural networks are introduced to better understand how GNNs are combined with Bayesian. Then, the development of Bayesian graph neural networks(BGNNs) in recent years is summarized, and its application in the field of engineering technology is demonstrated. Finally, the future development of BGNNs is prospected and the full text is summarized.