Graph Normalizing Flows
Machine Learning
2019-05-31 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1905.13177,
title = {Graph Normalizing Flows},
author = {Jenny Liu and Aviral Kumar and Jimmy Ba and Jamie Kiros and Kevin Swersky},
journal= {arXiv preprint arXiv:1905.13177},
year = {2019}
}