BuB:在知识图上进行链接预测的构建器-增强器模型
Mohammad Ali Soltanshahi1, Babak Teimourpour1, Hadi Zare2
1Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
概括
本研究介绍了用于链接预测 (LP) 的BuB模型,使用歧视性微调 (DFT) 解决一对多和多对多关系中的挑战. 这种BuB模式增强了关系的建立和加强,优于现有的方法.
科学领域:
- 图形神经网络的神经网络
- 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) 在各种领域至关重要,但现有的模型在一对多和许多对许多关系方面扎.
- 歧视性微调 (DFT),调整模型部件的学习率,尚未对LP进行探索.
- 处理复杂的关系结构仍然是链接预测的一个重大挑战.
研究的目的:
- 介绍一个新的模型,BuB,旨在有效地处理链接预测中的一对多和多对多关系.
- 首次探索歧视性微调 (DFT) 在链接预测中的应用.
- 增强解决方案空间并提高链接预测模型的性能.
主要方法:
- 拟议的BuB模型由两个组成部分组成:一个关系Builder和一个关系Booster.
- 排名函数以极坐标与第n根重新构成,以管理复杂的关系.
- 差别微调 (DFT) 用于调整学习率,强调构建器组件.
主要成果:
- 在链接预测中,BuB模型成功地解决了一对多和多对多的关系挑战.
- 使用极坐标和第n根扩大了最佳解决方案空间.
- 实验结果表明,BuB模型超过了对基准数据集的最先进方法.
结论:
- 结合DFT和新的排名函数的BuB模型在链接预测方面取得了重大进展.
- 该方法有效地处理复杂的关系结构,提高预测准确度.
- 这项研究为在基于图形的机器学习任务中应用DFT开辟了新的途径.
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