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Found 3 papers, 1 papers with code
Variational DAG Estimation via State Augmentation With Stochastic Permutations
Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
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 attacks.
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