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

We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.