重温初始化然后再精细化:一个不完整和缺失的图形推算网络
IEEE transactions on neural networks and learning systems
|January 12, 2024
概括
本研究引入了一个新的图形归算网络 (RITR),以解决图形中缺少的数据. 该方法有效地处理不完整和缺失的属性,在实验中表现优于现有技术.
科学领域:
- 图表 机器学习 机器学习
- 数据挖掘 数据挖掘
- 网络科学 网络科学
背景情况:
- 图形数据在推系统和社交网络分析等应用中很普遍.
- 图形数据经常因隐私或版权问题而缺少属性,被分类为属性不完整或属性缺失.
- 现有的图表归算方法不能同时处理两种类型的缺失数据的场景.
研究的目的:
- 开发一个新的图形归算网络 (RITR),能够处理混合缺失图形 (属性不完整和属性缺失).
- 为了有效地完成图形数据,引入初始化-然后-精细化归算标准.
- 提供第一个端到端的无监督框架,用于混合缺席图形归算.
主要方法:
- 对于属性不完整的样本:以高斯噪声初始化,然后使用结构属性一致性约束来完善.
- 对于缺少属性样本:初始化结构嵌入,然后通过通过动态亲和结构将属性不完整样本的信息汇总成精细化.
- 对于这两种数据类型,RITR网络采用了初始化然后精制的策略.
主要成果:
- RITR成功地归因了属性不完整和属性缺失的图形数据.
- 六个数据集的实验表明,RITR的表现始终优于最先进的竞争对手.
- 拟议的方法在处理混合缺席图表时实现了卓越的性能.
结论:
- 开发的RITR网络是一个有效的端到端无监督框架,用于混合缺席图形归算.
- RITR解决了现有的图形归算技术中的一个重大缺口.
- 该方法显示了对涉及不完整图形数据的现实世界应用的巨大潜力.
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