DII-GCN: Dropedge Based Deep Graph Convolutional Networks

Abstract

Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a significant obstacle for non-grid graphs. In particular, because of the over-smoothing problem, most existing GCNs are only effective below four layers. This work proposes a novel GCN named DII-GCN that originally integrates Dropedge, Initial residual, and Identity mapping methods into traditional GCNs for mitigating over-smoothing. In the first step of the DII-GCN, the Dropedge increases the diversity of learning sample data and slows down the network’s learning speed to improve learning accuracy and reduce over-fitting. The initial residual is embedded into the convolutional learning units under the identity mapping in the second step, which extends the learning path and thus weakens the over-smoothing issue in the learning process. The experimental results show that the proposed DII-GCN achieves the purpose of constructing deep GCNs and obtains better accuracy than existing shallow networks. DII-GCN model has the highest 84.6% accuracy at 128 layers of the Cora dataset, highest 72.5% accuracy at 32 layers of the Citeseer dataset, highest 79.7% accuracy at 32 layers of the Pubmed dataset.

Keywords

• graph neural network
• DropEdge
• over-fitting
• over-smoothing