  Graph Neural Networks (GNNs) have become increasingly popular and achieved
impressive results in many graph-based applications. However, extensive manual
work and domain knowledge are required to design effective architectures, and
the results of GNN models have high variance with different training setups,
which limits the application of existing GNN models. In this paper, we present
AutoHEnsGNN, a framework to build effective and robust models for graph tasks
without any human intervention. AutoHEnsGNN won first place in the AutoGraph
Challenge for KDD Cup 2020, and achieved the best rank score of five real-life
datasets in the final phase. Given a task, AutoHEnsGNN first applies a fast
proxy evaluation to automatically select a pool of promising GNN models. Then
it builds a hierarchical ensemble framework: 1) We propose graph self-ensemble
(GSE), which can reduce the variance of weight initialization and efficiently
exploit the information of local and global neighborhoods; 2) Based on GSE, a
weighted ensemble of different types of GNN models is used to effectively learn
more discriminative node representations. To efficiently search the
architectures and ensemble weights, we propose AutoHEnsGNN$_{\text{Gradient}}$,
which treats the architectures and ensemble weights as architecture parameters
and uses gradient-based architecture search to obtain optimal configurations,
and AutoHEnsGNN$_{\text{Adaptive}}$, which can adaptively adjust the ensemble
weight based on the model accuracy. Extensive experiments on node
classification, graph classification, edge prediction and KDD Cup challenge
demonstrate the effectiveness and generality of AutoHEnsGNN
