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适应性邻居选择图形嵌入的信息流优化.

Yufei Wu1, Xiumei Wen1,2, Wei Chen1

  • 1Hebei University of Architecture, School, Zhangjiakou, 075000, Hebei, China.

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概括
此摘要是机器生成的。

通过优化信息流和选择潜在的邻居,GraphFlow增强了异质图表表示学习. 这提高了节点表示的独特性和任务性能,特别是在复杂的图形中.

关键词:
不同质的图形表示学习学习.适应性元路径生成图表 卷积网络 卷积网络信息流的优化信息流的优化.邻居选择机制 邻居选择机制

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形表示学习学习学习图形表示学习

背景情况:

  • 由于依赖固定元路径和邻近聚合,传统方法难以处理异质图.
  • 信息稀缺和有限的邻居导致过度平滑和减少代表能力.
  • 现有的方法往往无法适应性地捕捉复杂的节点间关联.

研究的目的:

  • 提出GraphFlow,一个用于异质图表表示学习的新型框架.
  • 通过优化信息传播和适应性邻居选择来增强节点间的关联学习.
  • 为了提高节点表示的独特性和模型概括性.

主要方法:

  • 信息流的优化和潜在的邻居选择机制.
  • 整合HodgeRank排名以精细选择邻居.
  • 适应性元路径生成用于建模深度语义关系.

主要成果:

  • 在节点分类和链接预测任务上,GraphFlow显著优于基线方法.
  • 在复杂的异质图数据集上表现出卓越的性能.
  • 显著提高了模型的独特性和概括能力.

结论:

  • GraphFlow有效地解决了异质图表表示学习中传统方法的局限性.
  • 该框架的自适应方法增强了信息传播和邻居发现.
  • 结果验证了GraphFlow在改善复杂图形结构的表示学习方面的有效性.