Improving the Long-Range Performance of Gated Graph Neural Networks

• URL: http://arxiv.org/abs/2007.09668v1
• Date: Sun, 19 Jul 2020 13:25:38 GMT
• Title: Improving the Long-Range Performance of Gated Graph Neural Networks
• Abstract summary: We propose a novel GNN architecture 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.
• Score: 29.84360748077222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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