Title: Graphite: Iterative Generative Modeling of Graphs,
Abstract: Graphs are a fundamental abstraction for modeling relational data. However,
graphs are discrete and combinatorial in nature, and learning representations
suitable for machine learning tasks poses statistical and computational
challenges. In this work, we propose Graphite an algorithmic framework for
unsupervised learning of representations over nodes in a graph using deep
latent variable generative models. Our model is based on variational
autoencoders (VAE), and uses graph neural networks for parameterizing both the
generative model (i.e., decoder) and inference model (i.e., encoder). The use
of graph neural networks directly incorporates inductive biases due to the
spatial, local structure of graphs directly in the generative model. We draw
novel connections of our framework with approximate inference via kernel
embeddings. Empirically, Graphite outperforms competing approaches for the
tasks of density estimation, link prediction, and node classification on
synthetic and benchmark datasets.