通过绘制生成过程的草图来学习节点表示与事件的好处 链接预测在异质网络上的好处
概括
本研究介绍了通过异质信息网络 (CLEH) 的事件进行对比学习,以改善链接预测. 通过建模事件生成过程,CLEH捕捉了异质信息网络中的更高阶交互.
科学领域:
- 图形表示学习学习学习图形表示学习
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 异质信息网络 (HIN) 对于模拟复杂的现实世界交互至关重要.
- 在HIN上的表示学习为网络分析和机器学习产生了紧的嵌入式.
- 现有的方法往往忽视了HIN的事件驱动性,限制了它们捕获更高阶交互和预测未来联系的能力.
研究的目的:
- 开发一种新的代表性学习方法,以解决现有方法的局限性.
- 增强HIN嵌入的功能,以保持更高层次的交互,并预测潜在的链接.
- 提高异质信息网络中链接预测任务的性能.
主要方法:
- 通过异质信息网络 (CLEH) 活动提出对比学习.
- 将HIN生成过程从局部节点结构划定为更高阶事件结构.
- 设计一个事件级别的对比学习程序,以捕捉高阶节点关系.
- 使用规范化流量模型作为编码器来增强嵌入表达力.
主要成果:
- 通过建模事件生成过程,CLEH有效地捕捉了节点之间的高阶关系.
- 与现有的基线相比,拟议的方法在链接预测任务中显示出明显的优势.
- 由CLEH生成的嵌入更具表达性,因为使用了规范化流量模型.
结论:
- 对于异质信息网络来说,CLEH在表示学习方面取得了重大进展.
- 以事件为中心的方法有效地模拟了更高阶的相互作用,从而改善了链接预测.
- 通过结合它们的生成动态,CLEH提供了对HIN的更全面的理解.
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