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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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对于异质图形神经网络的路径感知多尺度学习.

Jin Fan1, Jiajun Yang2, Zhangyu Gu2

  • 1Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China; Zhejiang Key Laboratory of New Industrial Internet Control Technology, Hangzhou Dianzi University, Hangzhou, China; Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Hangzhou, China.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

这项研究介绍了PM-HGNN,一种新的异质图形神经网络. 通过减少元路径冗余和利用全球信息,PM-HGNN改善了节点分类.

关键词:
图形神经网络是一个神经网络.不同质的图形表示学习学习.的元路径.

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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:

  • 不同质的图形神经网络 (HGNNs) 模拟具有不同节点/边缘类型的复杂数据.
  • 基于元路径的HGNN提供性能和可解释性,但往往忽视元路径冗余和全球信息.
  • 由于路径特征和全球背景的有限利用,现有的HGNN在全面的表示学习中扎.

研究的目的:

  • 提出PM-HGNN,一个路径意识的多尺度异质图形神经网络.
  • 解决元路径冗余的局限性和当前HGNN中不足的全球信息利用问题.
  • 为了增强对异质图的表示学习.

主要方法:

  • PM-HGNN使用基于全球相似性的平均聚合器进行预计算邻近聚合.
  • 它动态地赋予元路径权重,利用它们的相关性和差异来减少冗余.
  • 该模型整合了多个规模的信息和路径特征,以改善学习.

主要成果:

  • 在节点分类任务中,PM-HGNN始终优于最先进的方法.
  • 在四个现实世界异质图形数据集上的实验验验证了拟议的方法.
  • 该方法在捕捉复杂的图形结构方面表现出卓越的性能.

结论:

  • PM-HGNN有效地解决了现有的基于元路径的HGNN的局限性.
  • 拟议的方法通过整合全球信息和优化元路径利用来增强表示学习.
  • PM-HGNN代表了异构图神经网络研究的重大进展.