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纯节点选择不平衡的图节点分类的纯节点选择.

Fanlong Zeng1, Wensheng Gan1, Jiayang Wu2

  • 1School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, 519070, China.

Neural networks : the official journal of the International Neural Network Society
|November 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了纯节点采样 (PNS),以解决图形神经网络 (GNN) 中的随机异常连接问题 (RACP). PNS是一个插入运行模块,通过减轻随机种子和不平衡数据引起的问题来稳定GNN性能.

关键词:
数据增强,数据增强.图形挖掘是指挖掘图形的过程.不平衡的学习学习.节点的分类 节点的分类

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科学领域:

  • 图形神经网络的神经网络
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 类不平衡,即数据在类之间分布不均,是机器学习中的一个常见问题,特别影响图形结构数据.
  • 图形神经网络 (GNN) 经常假设类平衡,当面对不平衡的数据集时,导致性能下降.
  • 现有的方法难以解决数量和拓不平衡,并且由于GNN中的随机种子灵敏性,出现了称为随机异常连接问题 (RACP) 的特定问题.

研究的目的:

  • 识别和解决由随机种子敏感性引起的图形神经网络 (GNN) 中的随机异常连接问题 (RACP).
  • 提出一种新的,插即用模块,可以缓解RACP,而不需要针对数量或拓不平衡的专用算法.
  • 在不平衡图形数据集上增强GNN的稳定性和性能.

主要方法:

  • 提出纯节点采样 (PNS),这是一个为节点合成阶段设计的新的插即用模块.
  • PNS在节点合成过程中直接运行,以减轻RACP和缓解异常邻近分布导致的性能退化.
  • 进行了广泛的实验,以分析随机种子对 GNN 性能的影响,并验证 PNS 的有效性.

主要成果:

  • 证明纯节点采样 (PNS) 有效地消除了不利的随机种子引起的性能下降.
  • 在各种基准数据集和不同的GNN骨干中,PNS显著优于基线方法.
  • 实验结果证实了PNS在处理类不平衡和RACP在图形数据中的有效性和稳定性.

结论:

  • 纯节点采样 (PNS) 是解决GNN中随机异常连接问题 (RACP) 的有效和稳定的解决方案.
  • PNS提供了一种多功能,插即用的方法,以提高GNN在不平衡图形数据集上的性能.
  • 拟议的方法提高了GNN对随机种子变异和异常数据分布的稳定性.