  Despite Graph neural networks' significant performance gain over many classic
techniques in various graph-related downstream tasks, their successes are
restricted in shallow models due to over-smoothness and the difficulties of
optimizations among many other issues. In this paper, to alleviate the
over-smoothing issue, we propose a soft graph normalization method to preserve
the diversities of node embeddings and prevent indiscrimination due to possible
over-closeness. Combined with residual connections, we analyze the reason why
the method can effectively capture the knowledge in both input graph structures
and node features even with deep networks. Additionally, inspired by Curriculum
Learning that learns easy examples before the hard ones, we propose a novel
label-smoothing-based learning framework to enhance the optimization of deep
GNNs, which iteratively smooths labels in an auxiliary graph and constructs
many gradual non-smooth tasks for extracting increasingly complex knowledge and
gradually discriminating nodes from coarse to fine. The method arguably reduces
the risk of overfitting and generalizes better results. Finally, extensive
experiments are carried out to demonstrate the effectiveness and potential of
the proposed model and learning framework through comparison with twelve
existing baselines including the state-of-the-art methods on twelve real-world
node classification benchmarks.
