GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

22 Apr 2019  ·  Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Node Classification Citeseer GraphNAS Accuracy 73.1 ± 0.9% # 38
Node Classification Cora GraphNAS Accuracy 84.2% ± 1.0% # 33
Node Classification PPI GraphNAS F1 98.6 ± 0.1 # 13
Node Classification Pubmed GraphNAS Accuracy 79.6 ± 0.4% # 41

Methods