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

• URL: http://arxiv.org/abs/2105.08330v1
• Date: Tue, 18 May 2021 07:52:51 GMT
• Title: Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
• Authors: Huixuan Chi, Yuying Wang, Qinfen Hao, Hong Xia
• Abstract summary: 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. Experiments on Open Graph Benchmark show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%2.84%.
• Score: 0.38233569758620045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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