# Gated Graph Convolutional Recurrent Neural Networks

## TL;DR

This paper introduces Gated Graph Convolutional Recurrent Neural Networks (GCRNNs) designed for graph-based problems like earthquake epicenter detection and weather prediction, offering improved performance and parameter efficiency.

## Contribution

It presents a novel GCRNN architecture with a gated variation, enhancing graph sequence modeling with fewer parameters and better accuracy than existing methods.

## Key findings

- GCRNNs outperform existing GNNs and graph recurrent models in experiments.
- GCRNNs use convolutional filter banks to keep parameters independent of graph size.
- Gated GCRNNs further improve performance with a time-gating mechanism.

## Abstract

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.