TL;DR本研究提出自动设计深层 GNN 的方法,其中添加了新型的跳跃连接以促进特征重用和缓解梯度消失问题,并允许进化算法在演化过程中增加 GNN 层数来生成更深的网络,在 Cora、Citeseer、Pubmed 和 PPI 数据集上实验表明,GNNs 的生成结果具有最先进的性能。
Abstract
Graphs play an important role in many applications. Recently, Graph Neural
Networks (GNNs) have achieved promising results in graph analysis tasks. Some
state-of-the-art gnn models have been proposed, e.g., Graph Convolutional
Networks (GCNs), Graph Attention Networks (GATs), etc. Desp