# Gated Graph Convolutional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1903.01888 · HTML: https://mlsanity.com/paper/1903.01888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Learning by Transference: Training Graph Neural Networks on Growing Graphs](https://mlsanity.com/paper/2106.03693/) (2021)
- [GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy](https://mlsanity.com/paper/2104.10569/) (2021)
- [Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2104.13096/) (2021)
- [Graph Feature Gating Networks](https://mlsanity.com/paper/2105.04493/) (2021)
- [ResVGAE: Going Deeper with Residual Modules for Link Prediction](https://mlsanity.com/paper/2105.00695/) (2021)
- [HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction](https://mlsanity.com/paper/2509.02481/) (2025)

## Abstract (arXiv)

Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems. GCRNNs use convolutional filter banks to keep the number of trainable parameters independent of the size of the graph and of the time sequences considered. We also put forward Gated GCRNNs, a time-gated variation of GCRNNs akin to LSTMs. When compared with GNNs and another graph recurrent architecture in experiments using both synthetic and real-word data, GCRNNs significantly improve performance while using considerably less parameters.