Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
This work addresses the need for better performance in node classification tasks for graph-based machine learning, but it is incremental as it builds on existing GCN techniques.
The paper tackles the problem of improving node classification accuracy in Graph Convolutional Networks (GCNs) by proposing two new tricks, GCN_res Framework and Embedding Usage, which increase test accuracy by 1.21% to 2.84% on the Open Graph Benchmark datasets.
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.