利用相似性和基于社区的功能来预测时间社交网络中的链接,使用启用注意力的LSTM
Rohit Ahuja1, Simranjit Kaur1, Harish Kumar Shakya2
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
Scientific reports
|December 27, 2025
概括
本研究介绍了SimCom-AN-LSTM,这是一种用于动态社交网络链接预测的新框架. 它有效地结合了结构和社区特征,使用长短期记忆 (LSTM) 模型来提高预测准确性.
科学领域:
- 计算机科学 计算机科学
- 网络科学 网络科学
- 人工智能的人工智能
背景情况:
- 由于网络结构不断变化,在动态的社交网络中进行链接预测具有挑战性.
- 传统的基于相似性的方法使用单一的快照错过时间模式.
- 社区检测可以通过专注于社区内部链接来提高链接预测.
研究的目的:
- 提出一个新的框架,SimCom-AN-LSTM,用于动态的社交网络链接预测.
- 利用结合的结构和社区特征来提高预测准确度.
- 捕捉在不断发展的网络中的时间依赖性和局部相互作用.
主要方法:
- 使用长短期内存 (LSTM) 模型来处理网络快照序列.
- 整合结构和社区特征以捕捉网络动态.
- 整合了注意力机制,以优先考虑信息特征进行预测.
主要成果:
- 与单个最先进的算法相比,SimCom-AN-LSTM框架显示出更高的性能.
- 结合结构和社区特征,显著提高了链接预测准确度.
- LSTM模型有效地学习了时间模式和网络演变.
结论:
- 拟议的SimCom-AN-LSTM框架为动态社交网络链接预测提供了一种有效的方法.
- 整合多种功能和时间建模可以提高对未来链接的预测.
- 注意力机制进一步提高了基于序列的模型在网络分析中的性能.
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