Graph Normalizing Flows Article Swipe

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Computer science Graph Memory footprint Theoretical computer science Autoencoder Algorithm Artificial neural network Artificial intelligence Operating system

Jenny Liu , Aviral Kumar , Jimmy Ba , Jamie Kiros , Kevin Swersky ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1905.13177 · OA: W2947204876

We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.