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使用低维节点嵌入的链接预测:测量问题

Nicolas Menand¹, C Seshadhri²

¹Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104.

Proceedings of the National Academy of Sciences of the United States of America

|February 16, 2024

概括

用于链接预测的图形表示学习方法显示有偏见的性能. 新的以顶点为中心的测量揭示了低维嵌入与稀疏的网络数据作斗争,挑战现有结果.

科学领域:

机器学习机器学习
网络科学网络科学
数据挖掘数据挖掘

背景情况:

图形表示学习 (GRL) 对于复杂网络上的机器学习 (ML) 至关重要,为顶点生成低维向量嵌入.
链接预测是GRL的一个关键下游任务,最近的研究报告了使用AUC (曲线下的面积) 等指标的高性能.

研究的目的:

调查标准性能指标中的偏差,用于链接预测中的图形表示学习.
引入和使用一个新的以顶点为中心的性能测量方法 (VCMPR@k图) 来重新评估链接预测模型.
从理论上分析低维嵌入在捕捉稀疏网络结构中的局限性.

主要方法:

开发了VCMPR@k图像作为用于链接预测的以顶点为中心的评估指标.
经验评估了现有的图表表示学习方法,使用AUC和VCMPR@k图.
建立了一个正式的理论框架,连接嵌入几何学,数据稀疏性和预测性能.

主要成果:

用于链接预测的标准AUC得分是有偏见的,并且可以掩盖在稀疏的基准真实数据上的糟糕表现.
使用图形表示的链接预测器在与VCMPR@k图表进行评估时表现不佳,尽管AUC很高.
从理论上证明,具有点积相似性的低维嵌入不能有效地捕捉稀疏的基本真相.

结论:

现有的对图形表示学习进行链接预测的评估可能由于指标偏差而不可靠.
VCMPR@k图表强调了使用低维节点嵌入用于稀疏网络上的链接预测的重大科学挑战.
需要进一步的研究来开发GRL方法,可以有效地处理稀疏的网络结构.

关键词:

图形嵌入式图形嵌入式图表表示学习学习的表现.链接预测链接预测低尺寸的嵌入方式机器学习指标机器学习指标

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