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不同质的图形嵌入与双边分化.

Yuhong Chen1, Fuhai Chen1, Zhihao Wu1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China.

Neural networks : the official journal of the International Neural Network Society
|December 11, 2024
PubMed
概括
此摘要是机器生成的。

带有双边差异化 (HGE-DED) 的异质图嵌入引入了灵活的元路径构建,以捕捉不同的节点关系. 这种方法改善了语义学习,并优于对基准数据集的现有方法.

关键词:
图表神经网络的神经网络不同质的信息网络 不同质的信息网络一个元路径组合组合.语义嵌入是一种语义嵌入.半监督的分类是半监督的分类

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科学领域:

  • 图形表示学习学习学习图形表示学习
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 异质图对于模拟各种实体和关系的真实世界数据至关重要.
  • 现有的基于元路径的方法往往忽视了元路径类型和范围的多样性,限制了语义学习.
  • 传统方法中预先计算的路径固定的性质忽视了微妙的节点相关性.

研究的目的:

  • 提出一种新的基于元路径的语义嵌入方案,即带有双边分化 (HGE-DED) 的异构图嵌入.
  • 通过构建灵活的元路径组合来增强丰富和歧视性节点语义的学习.
  • 解决现有的异质图嵌入方法中固定元路径构建的局限性.

主要方法:

  • 开发了多种类型和多种范围的元路径构造 (MTR-MP构造) 用于全面的元路径探索.
  • 嵌入式语义和元路径联合指导,用于层次的短期和长期关系调整.
  • 设计了一种双边差异化机制,以更好地代表细粒度尺度上的边缘多样性.

主要成果:

  • HGE-DED有效地构建灵活的元路径组合,捕获多样化的语义信息.
  • 与最先进的方法相比,该方法在学习歧视性节点嵌入方面表现出卓越的表现.
  • 在四个基准数据集上的实验结果验证了拟议的HGE-DED方案的有效性.

结论:

  • 通过拥抱元路径多样性,HGE-DED为异质图嵌入提供了更强大的方法.
  • 提出的方法成功地减轻了异构图中边缘异构的影响.
  • 这项工作通过提供对异质图形结构的更细致的理解,推进了图形表示学习领域.