论文标题
改善封闭图形神经网络的远程性能
Improving the Long-Range Performance of Gated Graph Neural Networks
论文作者
论文摘要
能够处理多关系图的许多流行的图形神经网络（GNN）的流行变体可能会因梯度消失而遭受。在这项工作中，我们提出了一种基于门控图神经网络的新型GNN体系结构，其能力提高了在多关系图中处理长期依赖性的能力。对不同合成任务的实验分析表明，所提出的体系结构的表现优于几个流行的GNN模型。
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.
