Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks

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 only visible in source code. this paper, we first summarize some existing effective used GCNs mini-batch training. Based this, two novel named GCN_res Framework Embedding Usage by leveraging residual network pre-trained embedding improve baseline's test accuracy different datasets. Experiments Open Benchmark (OGB) show that, combining these techniques, of various increases 1.21%~2.84%. We open our at https://github.com/ytchx1999/PyG-OGB-Tricks.