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DropNaE:减轻大规模图形表示学习的不规则性

Xin Liu1, Xunbin Xiong2, Mingyu Yan1

  • 1SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|December 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了DropNaE,这是一种在训练图形神经网络 (GNN) 之前减少图形不规则性的方法. 通过简化图形结构以实现更快的GPU处理,DropNaE提高了GNN的效率和准确性.

关键词:
在图形表示学习算法上的算法.有效的大规模图表表示学习学习.违规行为 违规行为

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科学领域:

  • 图形神经网络 (GNN) 是一个神经网络.
  • 在GPU计算中使用GPU计算.
  • 数据科学是数据科学.

背景情况:

  • 大规模图形在现实世界应用中很常见.
  • 图形神经网络 (GNN) 对于在GPU上处理图形数据非常有效.
  • 图形的不规则性阻碍了GNN训练期间的GPU效率.

研究的目的:

  • 为解决由图形不规则引起的GNN在GPU上的训练效率低下的问题.
  • 提出一种新的方法,DropNaE,以减轻图形数据的不规则性.
  • 提高GNN的训练速度和准确性.

主要方法:

  • 开发了一个度量来量化节点邻近异构性.
  • 引入了DropNaE的两个变体,以减少图形的不规则性.
  • DropNaE通过条件下降节点和边缘预处理图形.
  • 将不规则的度分布转换为更均的分布.

主要成果:

  • DropNaE有效地减轻了大尺度图表中的不规则性.
  • 该方法与流行的GNN架构兼容.
  • 实验表明,培训效率和准确性都得到了改善.
  • DropNaE是一个线下过程,不需要在线计算资源.

结论:

  • DropNaE为当前和未来的最先进的GNN提供了显著的好处.
  • 拟议的方法通过简化图形结构来提高GNN的性能.
  • DropNaE为高效的大规模图形处理提供了一个实用的解决方案.