  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.
