Oversmoothing is a commonly observed challenge in graph neural network (GNN)
learning, where, as layers increase, embedding features learned from GNNs
quickly become similar/indistinguishable, making them incapable of
differentiating network proximity. A GNN with shallow layer architectures can
only learn short-term relation or localized structure information, limiting its
power of learning long-term connection, evidenced by their inferior learning
performance on heterophilous graphs. Tackling oversmoothing is crucial to
harness deep-layer architectures for GNNs. To date, many methods have been
proposed to alleviate oversmoothing. The vast difference behind their design
principles, combined with graph complications, make it difficult to understand
and even compare their difference in tackling the oversmoothing. In this paper,
we propose ATNPA, a unified view with five key steps: Augmentation,
Transformation, Normalization, Propagation, and Aggregation, to summarize GNN
oversmoothing alleviation approaches. We first outline three themes to tackle
oversmoothing, and then separate all methods into six categories, followed by
detailed reviews of representative methods, including their relation to the
ATNPA, and discussion about their niche, strength, and weakness. The review not
only draws in-depth understanding of existing methods in the field, but also
shows a clear road map for future study.

在本研究中，我们提出了 ATNPA（增强、转化、归一化、传播和聚合），总结了图神经网络（GNN）过度平滑的缓解方法，包括三个主题和六个类别的方法，并详细审查了代表性方法，包括它们与 ATNPA 的关系以及其优劣势。这一综述不仅深入了解了该领域现有方法，还为未来研究提供了明确的路线图。