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Found 4 papers, 3 papers with code
Improving the interpretability of GNN predictions through conformal-based graph sparsification
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks.
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces.
Learnable Graph Convolutional Attention Networks
Existing Graph Neural Networks (GNNs) compute the message exchange between nodes by either aggregating uniformly (convolving) the features of all the neighboring nodes, or by applying a non-uniform score (attending) to the features.
VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available.
