  Recent years have witnessed the popularity and success of graph neural
networks (GNN) in various scenarios. To obtain data-specific GNN architectures,
researchers turn to neural architecture search (NAS), which has made impressive
success in discovering effective architectures in convolutional neural
networks. However, it is non-trivial to apply NAS approaches to GNN due to
challenges in search space design and the expensive searching cost of existing
NAS methods. In this work, to obtain the data-specific GNN architectures and
address the computational challenges facing by NAS approaches, we propose a
framework, which tries to Search to Aggregate NEighborhood (SANE), to
automatically design data-specific GNN architectures. By designing a novel and
expressive search space, we propose a differentiable search algorithm, which is
more efficient than previous reinforcement learning based methods. Experimental
results on four tasks and seven real-world datasets demonstrate the superiority
of SANE compared to existing GNN models and NAS approaches in terms of
effectiveness and efficiency. (Code is available at:
https://github.com/AutoML-4Paradigm/SANE).
