Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Pantelis Elinas, Edwin V. Bonilla, Louis Tiao

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial a…