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    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.