在时间网络上进行感应链预测的远程意识学习
IEEE transactions on neural networks and learning systems
|November 16, 2023
概括
我们介绍了一种远程意识学习 (DEAL) 方法,用于对时间网络的诱导链接预测. 在嵌入式和动态图形结构中,DEAL通过测量节点距离来提高准确性,特别是在稀疏网络中.
科学领域:
- 计算机科学 计算机科学
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 时间网络上的诱导链接预测预测了未见节点的未来连接.
- 当前的方法通常依赖于节点属性或共同邻居,这些属性并不总是可用或可靠,尤其是在稀疏的网络中.
研究的目的:
- 提出一种新的远程意识学习 (DEAL) 方法,用于对时间网络的诱导链接预测.
- 解决现有方法的局限性,这些方法依赖于节点属性或与稀疏的时间网络结构作斗争.
主要方法:
- DEAL采用适应性采样方法来提取时间适应性步行,增强共同邻居的纳入.
- 双通道距离测量组件评估嵌入空间和动态图形结构中的距离.
- 该方法在MathOverflow,AskUbuntu和StackOverflow数据集上进行了评估.
主要成果:
- 在诱导性链接预测中,DEAL在最先进的基线上表现优越.
- 观察到精度,ROC曲线下的面积 (AUC) 和平均精度 (AP) 的改善.
- 该方法在数据有限的场景中表现出特别高的效率.
结论:
- 提出的DEAL方法为时间网络中的诱导链接预测提供了一个强大的解决方案.
- 它能够处理稀疏的数据和缺乏属性的能力使其适合于现实世界的应用.
- 通过在动态网络环境中提供更准确,更可靠的链接预测,DEAL在该领域取得了进展.
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