  Graph Convolutional Networks (GCNs) and subsequent variants have been
proposed to solve tasks on graphs, especially node classification tasks. In the
literature, however, most tricks or techniques are either briefly mentioned as
implementation details or only visible in source code. In this paper, we first
summarize some existing effective tricks used in GCNs mini-batch training.
Based on this, two novel tricks named GCN_res Framework and Embedding Usage are
proposed by leveraging residual network and pre-trained embedding to improve
baseline's test accuracy in different datasets. Experiments on Open Graph
Benchmark (OGB) show that, by combining these techniques, the test accuracy of
various GCNs increases by 1.21%~2.84%. We open source our implementation at
https://github.com/ytchx1999/PyG-OGB-Tricks.
