Multi-scale Graph Convolutional Networks with Self-Attention
This addresses a crucial issue in graph neural networks for researchers and practitioners, offering incremental improvements over existing GCN methods.
The paper tackles the over-smoothing problem in graph convolutional networks (GCNs) by proposing two novel multi-scale GCN frameworks that incorporate self-attention and multi-scale information, resulting in improved computational efficiency and prediction accuracy, with the model capable of scaling up to 64 layers.
Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data recently. In general, deep GCNs do not work well since graph convolution in conventional GCNs is a special form of Laplacian smoothing, which makes the representation of different nodes indistinguishable. In the literature, multi-scale information was employed in GCNs to enhance the expressive power of GCNs. However, over-smoothing phenomenon as a crucial issue of GCNs remains to be solved and investigated. In this paper, we propose two novel multi-scale GCN frameworks by incorporating self-attention mechanism and multi-scale information into the design of GCNs. Our methods greatly improve the computational efficiency and prediction accuracy of the GCNs model. Extensive experiments on both node classification and graph classification demonstrate the effectiveness over several state-of-the-art GCNs. Notably, the proposed two architectures can efficiently mitigate the over-smoothing problem of GCNs, and the layer of our model can even be increased to $64$.