在异质信息网络中用于更高阶关系预测的反事实学习
Xuan Guo1, Jie Li1, Pengfei Jiao2
1Tianjin University, Tianjin, 300350, China.
概括
本研究介绍了HINCHOR,这是一种用于预测异质信息网络 (HINs) 中复杂多实体关系的新型模型. 亨科尔独特地使用反事实学习来通过考虑因果关系来改善更高阶关系预测.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 网络科学 网络科学
背景情况:
- 异质信息网络 (HIN) 对复杂系统的建模至关重要,链接预测是关键任务.
- 现有的方法往往忽略了多实体相互作用和全球图形结构对关系预测的因果影响.
- 涉及多个实体的更高阶关系是常见的,但在当前的研究中未得到充分探索.
研究的目的:
- 开发一个端到端模型,用于HIN中高阶关系预测.
- 通过结合多实体近距离和因果推理来解决现有方法的局限性.
- 提高复杂网络中关系预测的准确性和稳定性.
主要方法:
- 建议HINCHOR,一个具有更高阶结构编码器的模型,以捕捉多实体近距离.
- 实施反事实数据增强模块,以基于全球结构变化生成基于综合关系.
- 使用反事实学习来估计因果关系和改善关系预测.
主要成果:
- 与最先进的方法相比,HINCHOR在四个基准数据集上表现出更高的性能.
- 该模型有效地捕捉了HINs中更高阶的近距离信息和因果关系.
- 反事实学习提高了复杂的,多实体相互作用的预测.
结论:
- 对于异质信息网络来说,HINCHOR在高阶关系预测方面取得了重大进展.
- 反事实学习的整合为理解网络关系中的因果因素提供了更强大的方法.
- 这项工作为分析和预测现实世界系统中的复杂相互作用开辟了新的途径.
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