Variational Graph Recurrent Neural Networks
This is a PyTorch implementation of the VGRNN model as described in our paper:
E. Hajiramezanali*, A. Hasanzadeh*, N. Duffield, K. R. Narayanan, M. Zhou, and X. Qian, Variational Graph Recurrent Neural Networks, Advances in Neural Information Processing Systems (NeurIPS), 2019, *equal contribution
Abstract: Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph recurrent neural network (GRNN) to capture both topology and node attribute changes in dynamic graphs. We argue that the use of high-level latent random variables in this variational GRNN (VGRNN) can better capture potential variability observed in dynamic graphs as well as the uncertainty of node latent representation. With semi-implicit variational inference developed for this new VGRNN architecture (SI-VGRNN), we show that flexible non-Gaussian latent representations can further help dynamic graph analytic tasks. Our experiments with multiple real-world dynamic graph datasets demonstrate that SI-VGRNN and VGRNN consistently outperform the existing baseline and state-of-the-art methods by a significant margin in dynamic link prediction.
Example: VGRNN learns interpretable latent representations.
Cite
Please cite our paper if you use this code in your own work:
@inproceedings{hajiramezanali2019variational,
title={Variational graph recurrent neural networks},
author={Hajiramezanali, Ehsan and Hasanzadeh, Arman and Narayanan, Krishna and Duffield, Nick and Zhou, Mingyuan and Qian, Xiaoning},
booktitle={Advances in Neural Information Processing Systems},
pages={10700--10710},
year={2019}
}