Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
This work addresses uncertainty in graph structures for graph-based learning tasks, representing an incremental improvement over existing Bayesian GCNN methods.
The paper tackled the problem of uncertainty in graph structure for graph convolutional neural networks (GCNN) by proposing a non-parametric generative model integrated into a Bayesian GCNN framework, achieving superior or comparable performance in benchmark node classification tasks.
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite their impressive performance, the techniques have a limited capability to incorporate the uncertainty in the underlined graph structure. In order to address this issue, a Bayesian GCNN (BGCN) framework was recently proposed. In this framework, the observed graph is considered to be a random realization from a parametric random graph model and the joint Bayesian inference of the graph and GCNN weights is performed. In this paper, we propose a non-parametric generative model for graphs and incorporate it within the BGCN framework. In addition to the observed graph, our approach effectively uses the node features and training labels in the posterior inference of graphs and attains superior or comparable performance in benchmark node classification tasks.