Graph Representation Learning

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Our proposed framework uses a joint probabilistic model and stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without input graph data, outperforming state-of-the-art algorithms on semi-supervised classification tasks.
Pantelis Elinas, Edwin v. Bonilla, Louis Tiao
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Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings