  Graph Convolution Networks (GCNs) are becoming more and more popular for
learning node representations on graphs. Though there exist various
developments on sampling and aggregation to accelerate the training process and
improve the performances, limited works focus on dealing with the dimensional
information imbalance of node representations. To bridge the gap, we propose a
method named Dimensional reweighting Graph Convolution Network (DrGCN). We
theoretically prove that our DrGCN can guarantee to improve the stability of
GCNs via mean field theory. Our dimensional reweighting method is very flexible
and can be easily combined with most sampling and aggregation techniques for
GCNs. Experimental results demonstrate its superior performances on several
challenging transductive and inductive node classification benchmark datasets.
Our DrGCN also outperforms existing models on an industrial-sized Alibaba
recommendation dataset.
