Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
翻译:用于分析社会网络数据和生物数据等非欧元数据的信息网络(GNNs)已被广泛使用。尽管它们取得了成功,但图形神经网络的设计需要大量手工工作和领域知识。在本文中,我们提议了图形神经结构搜索方法(GraphNAS 简称),以便能够在强化学习的基础上自动搜索最佳图形神经结构。具体地说,GaphNAS首先使用一个经常性网络生成可变长字符串,描述图形神经网络的结构,然后用强化学习对经常性网络进行培训,以最大限度地提高验证数据集中生成的结构的预期准确性。关于移植和感化学习环境中节点分类任务的广泛实验结果表明,GraphNAS可以在Cora、Citeseer、Pubmed引力网络和蛋白质互动网络上实现持续更好的业绩。关于节点分类任务,GregNAS可以设计一个与测试数据集中的最佳人造架构相对应的新网络结构。