Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Huixuan Chi, Yuying Wang, Qinfen Hao, Hong Xia · Journal of Physics Conference Series · 2022
Abstract 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 namedGCN_res FrameworkandEmbedding Usageare 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.