Variational Graph Recurrent Neural Networks - PyTorch
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.
Python==3.8.8
networkx==2.5
scipy==1.6.2
torch==1.10.2
torch-cluster==1.5.9
torch-geometric==2.0.3
torch-scatter==2.0.9
torch-sparse==0.6.12
torch-spline-conv==1.2.1
@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}
}