标签信息对比训练用于知识图上的节点重要性估计
IEEE transactions on neural networks and learning systems
|February 21, 2024
概括
标签知情对比预训 (LICAP) 通过优先考虑重要的节点来增强知识图上的节点重要性估计 (NIE). 这种方法可以提高未来或缺失节点得分的预测准确性.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 节点重要性估计 (NIE) 对知识图 (KG) 至关重要.
- 目前的NIE方法在预训练期间对待所有节点均等.
- 现实世界中的应用程序通常需要优先考虑非常重要的节点.
研究的目的:
- 引入一个新的预培训框架,标签知情对比培训 (LICAP),为NIE.
- 开发一种方法,更好地解释预训练期间节点的重要性.
- 提高预测未来或缺失节点重要性得分的准确性.
主要方法:
- 在LICAP中,使用了具有连续标签的对比学习框架.
- 一个顶级节点喜欢分层抽样策略,按重要性分组节点.
- 预测意识图表注意网络 (PreGATs) 用于预训练节点嵌入.
主要成果:
- 通过区分高和低重要性节点,LICAP有效地预训练节点嵌入.
- 预训练的嵌入器显著提高了现有的NIE方法的性能.
- 在回归和排名指标上都取得了最先进的结果.
结论:
- 对于知识图的节点重要性估计,LICAP提供了显著的进步.
- 拟议的方法通过考虑节点重要性等级来提供更细致的预训练方法.
- LICAP的有效性通过下游NIE任务的改进性能来证明.
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