A Graph Architecture Search Method Based On Grouped Operations

Luoyu Chen, Jungang Xu, Kun Jing, Yingfei Sun

Abstract: Graph data is ubiquitous in the real world and graph neural networks (GNNs) are effective for modeling the complex relationships and dependencies between the entities. However, it's difficult to design data-specific GNNs. Recently, researchers have started to apply neural architecture search (NAS) to design GNNs. In this work, we propose a graph architecture search method to decrease the instability with a large number of candidate operations. Following SANE(Search to Aggregate NEighborhood), we focus on searching to aggregate neighbourhoods but we divide the candidate operations into groups. We use a continuous relaxation of our search space and optimize the hyper-networks with a gradient-based algorithm. Extensive experiments on several node-level and graph-level tasks demonstrate that our method achieves a promising performance.