  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 $64$.
