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Despite Graph neural networks' significant performance gain over many classictechniques in various graph-related downstream tasks, their successes arerestricted in shallow models due to over-smoothness and the difficulties ofoptimizations among many other issues. In this paper, to alleviate theover-smoothing issue, we propose a soft graph normalization method to preservethe diversities of node embeddings and prevent indiscrimination due to possibleover-closeness. Combined with residual connections, we analyze the reason whythe method can effectively capture the knowledge in both input graph structuresand node features even with deep networks. Additionally, inspired by CurriculumLearning that learns easy examples before the hard ones, we propose a novellabel-smoothing-based learning framework to enhance the optimization of deepGNNs, which iteratively smooths labels in an auxiliary graph and constructsmany gradual non-smooth tasks for extracting increasingly complex knowledge andgradually discriminating nodes from coarse to fine. The method arguably reducesthe risk of overfitting and generalizes better results. Finally, extensiveexperiments are carried out to demonstrate the effectiveness and potential ofthe proposed model and learning framework through comparison with twelveexisting baselines including the state-of-the-art methods on twelve real-worldnode classification benchmarks.

Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs