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Graph Networks (GNs) enable the fusion of prior knowledge and relationalreasoning with flexible function approximations. In this work, a generalGN-based model is proposed which takes full advantage of the relationalmodeling capabilities of GNs and extends these to probabilistic modeling withVariational Bayes (VB). To that end, we combine complementary pre-existingapproaches on VB for graph data and propose an approach that relies ongraph-structured latent and conditioning variables. It is demonstrated thatNeural Processes can also be viewed through the lens of the proposed model. Weshow applications on the problem of structured probability density modeling forsimulated and real wind farm monitoring data, as well as on the meta-learningof simulated Gaussian Process data. We release the source code, along with thesimulated datasets.

Relational VAE: A Continuous Latent Variable Model for Graph Structured Data