  Training deep graph neural networks (GNNs) is notoriously hard. Besides the
standard plights in training deep architectures such as vanishing gradients and
overfitting, it also uniquely suffers from over-smoothing, information
squashing, and so on, which limits their potential power for encoding the
high-order neighbor structure in large-scale graphs. Although numerous efforts
are proposed to address these limitations, such as various forms of skip
connections, graph normalization, and random dropping, it is difficult to
disentangle the advantages brought by a deep GNN architecture from those
"tricks" necessary to train such an architecture. Moreover, the lack of a
standardized benchmark with fair and consistent experimental settings poses an
almost insurmountable obstacle to gauge the effectiveness of new mechanisms. In
view of those, we present the first fair and reproducible benchmark dedicated
to assessing the "tricks" of training deep GNNs. We categorize existing
approaches, investigate their hyperparameter sensitivity, and unify the basic
configuration. Comprehensive evaluations are then conducted on tens of
representative graph datasets including the recent large-scale Open Graph
Benchmark, with diverse deep GNN backbones. We demonstrate that an organic
combo of initial connection, identity mapping, group and batch normalization
attains the new state-of-the-art results for deep GNNs on large datasets. Codes
are available: https://github.com/VITA-Group/Deep_GCN_Benchmarking.
