The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(AdaBoosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors and integrate knowledge from different hops of neighbors into the network in an AdaBoost way. We also present the architectural difference between AdaGCN and existing graph convolutional methods to show the benefits of our proposal. Finally, extensive experiments demonstrate the state-of-the-art prediction performance and the computational advantage of our approach AdaGCN.

翻译：深图模型的设计仍有待调查,关键部分是如何以有效的方式探索和利用来自邻国不同潮流的知识。在本文中,我们提出一个新的像RNN那样的深图神经网络结构,将AdaBoost纳入网络计算中;拟议的称为AdaGCN~(AdaBoosting图集网络)的图象革命网络有能力有效地从高端邻国获取知识,并以AdaBoost方式将来自邻国不同潮流的知识融入网络。我们还介绍了AdaGCN与现有图象相联方法之间的建筑差异,以展示我们提案的好处。最后,广泛的实验显示了我们的方法AdaGCN的最新预测性能和计算优势。