过度平滑的另一个视角:缓解深度GNN中的语义过度平滑
概括
深度图形神经网络 (GNN) 由于过度平滑而面临性能问题. 一个新的稀疏聚合策略保留了节点语义结构,提高了深度GNN性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 在图形结构数据分析方面表现出色,但更深层次的模型的性能下降.
- "过度平滑"问题导致无法区分的节点嵌入,破坏语义结构,阻碍了GNN的开发.
研究的目的:
- 在深度GNN中调查过度平滑问题.
- 提出一种新的策略,以保护深度GNN中的语义结构,解决现有方法的局限性.
主要方法:
- 引入深度GNN的集群维护稀疏聚合策略.
- 这个plug-and-play策略使用加权的剩余连接来重新分配节点聚合范围,保留语义结构.
主要成果:
- 拟议的策略有效地保留了语义结构,减轻了过度平滑的负面影响.
- 实验表明,在深度GNN任务上,性能与最先进的方法相美.
结论:
- 集群维护稀疏聚合策略为深度GNN中的过度平滑问题提供了可行的解决方案.
- 这种方法通过在更深层次的架构中保持语义完整性来提高GNN性能.
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