Bayesian graph convolutional neural networks for semi-supervised classification

Understand

Recently, techniques for applying convolutional neural networks to graph-structured data have emerged.

• Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion.
• Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure.
• Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions.