  Despite the enormous success of Graph Convolutional Networks (GCNs) in
modeling graph-structured data, most of the current GCNs are shallow due to the
notoriously challenging problems of over-smoothening and information squashing
along with conventional difficulty caused by vanishing gradients and
over-fitting. Previous works have been primarily focused on the study of
over-smoothening and over-squashing phenomena in training deep GCNs.
Surprisingly, in comparison with CNNs/RNNs, very limited attention has been
given to understanding how healthy gradient flow can benefit the trainability
of deep GCNs. In this paper, firstly, we provide a new perspective of gradient
flow to understand the substandard performance of deep GCNs and hypothesize
that by facilitating healthy gradient flow, we can significantly improve their
trainability, as well as achieve state-of-the-art (SOTA) level performance from
vanilla-GCNs. Next, we argue that blindly adopting the Glorot initialization
for GCNs is not optimal, and derive a topology-aware isometric initialization
scheme for vanilla-GCNs based on the principles of isometry. Additionally,
contrary to ad-hoc addition of skip-connections, we propose to use
gradient-guided dynamic rewiring of vanilla-GCNs} with skip connections. Our
dynamic rewiring method uses the gradient flow within each layer during
training to introduce on-demand skip-connections adaptively. We provide
extensive empirical evidence across multiple datasets that our methods improve
gradient flow in deep vanilla-GCNs and significantly boost their performance to
comfortably compete and outperform many fancy state-of-the-art methods. Codes
are available at: https://github.com/VITA-Group/GradientGCN.
