Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a joint probabilistic model with stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without …
Pantelis Elinas
