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    科学领域:

    • 机器学习 机器学习
    • 图形理论 图形理论
    • 数据挖掘 数据挖掘

    背景情况:

    • 现有的图形集群方法与固定的初始图形进行斗争,这些图形可能不代表数据拓.
    • 当初的图形结构不准确时,这种限制可能会导致低于最佳的集群性能.

    研究的目的:

    • 提出一个新的无监督的图形集群网络,EGRC-Net,通过学习嵌入来适应性地改进初始图形.
    • 通过整合语义和拓信息来提高集群精度和可扩展性.

    主要方法:

    • 利用自动编码器和图形卷积网络来学习潜在特征表示.
    • 动态地将初始的相邻矩阵与从嵌入空间的局部几何结构中衍生的新矩阵合并.
    • 采用Jeffreys的分歧进行无监督训练和改进的近似个性化传播以实现可扩展性.

    主要成果:

    • 在9个基准数据集上,EGRC-Net的表现始终优于最先进的方法.
    • 在DBLP数据集上,调整后兰德指数 (ARI) 取得了超过11.99%的改善.
    • 证明了可扩展性,ARI增益为10.73%,内存使用量减少了33.73%,运行时间减少了19.71%.

    结论:

    • EGRC-Net有效地改进了使用学习嵌入的初始图表,以获得卓越的集群性能.
    • 拟议的方法为无监督图形集群提供了一个可扩展和高效的解决方案.
    • 公开可用的代码有助于进一步的研究和应用.