Title: Gated Relational Graph Attention Networks. Abstract: Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs. Like all GNNs, they suffer from a drop in performance when training deeper networks, which may be caused by vanishing gradients, over-parameterization, and oversmoothing. Previous works have investigated methods that improve the training of deeper GNNs, which include normalization techniques and various types of skip connection within a node. However, learning long-range patterns in multi-relational graphs using GNNs remains an under-explored topic. In this work, we propose a novel GNN architecture based on the Graph Attention Network (GAT) that uses gated skip connections to improve long-range modeling between nodes and uses a more scalable vector-based approach for parameterizing relations. We perform an extensive experimental analysis on synthetic and real data, focusing explicitly on learning long-range patterns. The results indicate that the proposed method significantly outperforms several commonly used relational GNN variants when used in deeper configurations and stays competitive to existing architectures in a shallow setup.