Improving the Long-Range Performance of Gated Graph Neural Networks

2020/07/19 by Denis Lukovnikov, Jens Lehmann, Lukovnikov, Denis +3
Computer Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2007.09668

openalex publication_date 2020/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients. In this work, we propose a novel GNN architecture based on the Gated Graph Neural Network with an improved ability to handle long-range dependencies in multi-relational graphs. An experimental analysis on different synthetic tasks demonstrates that the proposed architecture outperforms several popular GNN models.

Citations

• On the difficulty of training Recurrent Neural Networks
• Gated Graph Sequence Neural Networks
• Neural Message Passing for Quantum Chemistry

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