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ATNPA: A Unified View of Oversmoothing Alleviation in Graph Neural Networks
May 6, 2024, 4:41 a.m. | Yufei Jin, Xingquan Zhu
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract architectures arxiv become challenge cs.ai cs.lg embedding features gnn gnns graph graph neural network graph neural networks layer learn making network networks neural network neural networks them type view
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