GraphPAS: Parallel Architecture Search for Graph Neural Networks

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021 · Jiamin Chen, Jianliang Gao, Yibo Chen, Oloulade Babatounde Moctard, Tengfei Lyu, Zhao Li ·

Graph neural architecture search has received a lot of attention as Graph Neural Networks (GNNs) has been successfully applied on the non-Euclidean data recently. However, exploring all possible GNNs architectures in the huge search space is too time-consuming or impossible for big graph data. In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning, which can improve the search efficiency without losing the accuracy. Additionally, architecture information entropy is adopted dynamically for mutation selection probability, which can reduce space exploration. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.