HKANLP:将预测与高层嵌入和科尔莫戈罗夫-阿诺德网络联系起来
IEEE transactions on neural networks and learning systems
|October 8, 2025
概括
链接预测的高层科尔摩戈罗夫-阿诺德网络 (HKANLP) 通过使用高层嵌入和科尔摩戈罗夫-阿诺德网络来增强图形自编码器. 这种新的方法可以提高各种图形类型的链接预测性能和稳定性.
科学领域:
- 图形神经网络 图形神经网络
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 链接预测 (LP) 对于基于图表的应用程序至关重要.
- 现有的图形自编码器 (GAE) 和变量 GAE (VGAE) 面临图形属性的限制,如负自值,阻碍性能.
- 在LP模型中的适应性和预测准确性仍然是关键挑战.
研究的目的:
- 引入高层科尔摩戈罗夫-阿诺德链路预测网络 (HKANLP),以克服当前GAE/VGAE的局限性.
- 通过解决内在的图形属性来增强链接预测,特别是在相邻矩阵中的负固有值.
- 提高基于图形的模型的适应性和预测性能.
主要方法:
- 拟议的HKANLP框架将图形神经网络 (GNN) 表示学习与高高层空间中的科尔摩戈罗夫-阿诺德网络 (KAN) 结合起来.
- 利用von Mises-Fisher (vMF) 分布在潜伏空间中的几何一致性.
- 采用KANs作为邻近矩阵重建的通用函数近似器,减轻负自值效应.
主要成果:
- HKANLP在同型,异型和大规模图形数据集中展示了优越的链接预测性能和稳定性.
- 在链接预测任务中,实验结果超过了最先进的基线.
- 可视化分析证实了该模型能够捕捉复杂的结构模式.
结论:
- 通过有效地处理挑战现有模型的图形属性,HKANLP在链接预测方面取得了重大进展.
- 该框架显示了改善光谱多样性和整体模型稳定性的前景.
- 拟议的方法为图形表示学习和链接预测应用提供了一个强大的新工具.
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