累积局部效应和图形神经网络用于链接预测
Paulina Kaczyńska1,2, Julian Sienkiewicz3,4, Dominik Ślęzak5
1Faculty of Mathematics, Informatics and Mechanics, Institute of Informatics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland. pm.kaczynska@student.uw.edu.pl.
Scientific reports
|February 12, 2026
概括
我们在链接预测中为图形神经网络 (GNN) 调整了累积局部效应 (ALE). 一种近似方法加快了分析速度,提供了与精确方法相似的解释,并提高了计算效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 在链接预测方面非常强大.
- 像累积局部效应 (ALE) 这样的无模型解释方法对于理解GNN至关重要.
- 由于复杂的节点相互作用,直接将ALE应用到GNN是计算密集的.
研究的目的:
- 调整累积局部效应 (ALE) 方法,用于在基于GNN的链接预测中可视化节点特征的影响.
- 通过提出一个近似的方法来解决将ALE应用于GNN的计算挑战.
主要方法:
- 研究了GNN (图形卷积网络和图形注意网络) 的累积局部效应 (ALE) 的适应.
- 开发和评估了一种近似方法,以减轻GNN中ALE的计算成本.
- 分析了参数变化对精确和近似方法的ALE估计准确性的影响.
主要成果:
- 与精确方法相比,近似ALE方法显著提高了计算效率.
- 精确的ALE方法提供了更稳定的解释,特别是在较小的数据集.
- 来自近似方法的解释与来自精确方法的解释相似.
结论:
- 大致ALE方法是解释链接预测任务中的GNN的一个可行和有效的方法.
- 计算效率和解释稳定性之间存在权衡,但近似方法提供了一个实际的解决方案.
- 对参数效应的进一步分析对于准确地估计GNN中的ALE很重要.
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