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Graphite: Iterative Generative Modeling of Graphs
Aditya Grover · Aaron Zweig · Stefano Ermon

Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Hall A

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 large graphs using deep latent variable generative models. Our model is based on a novel combination of graph neural networks with variational autoencoders (VAE), and uses an iterative graph refinement strategy for decoding. This permits scaling to large graphs with thousands of nodes. Theoretically, we draw novel connections of graph neural networks 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.

Author Information

Aditya Grover (Stanford University)
Aaron Zweig (Stanford University)
Stefano Ermon (Stanford University)

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