持久的同质性与机器学习相结合,用于社交网络活动分析.
Zhijian Zhang1,2, Yuqing Sun1, Yayun Liu1
1Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
本研究引入了一种新的方法,通过分析社交网络用户的活动水平来对其进行分类. 通过使用持久性来测量拓复杂性,研究人员可以有效地分组用户,提高我们对在线行为的理解.
科学领域:
- 社交网络分析 社交网络分析
- 计算拓学的计算拓学
- 机器学习 机器学习
背景情况:
- 社交媒体促进了用户频繁的沟通.
- 通过活动分类了解用户行为至关重要.
- 现有的方法可能无法完全捕捉复杂的网络动态.
研究的目的:
- 开发一种新的方法,根据社交网络用户的活动来对其进行分类.
- 用自我网络的拓特征来量化用户活动.
- 提高对社交网络用户行为的理解.
主要方法:
- 为个人用户构建自我网络.
- 使用持久性图和持久性同质性编码拓特征.
- 计算持久性和定义规范 (NE(X)) 以测量拓复杂性和用户活动.
- 用提取的特征向量来训练机器学习模型,用于用户分类.
主要成果:
- 拟议的规范 (NE(X)) 实际上代表了社交网络节点的拓复杂性和活动水平.
- 数字实验证明了算法的能力,有效地将用户分为不同的组.
- 该方法在评估集群质量时显示出有希望的结果,使用像配置系数这样的指标.
结论:
- 开发的算法为分类社交网络用户提供了有效的方法.
- 这种基于拓复杂性的分类为未来的社会网络分析研究和应用提供了坚实的基础.
- 该研究强调了应用持久性同质学的潜力,以了解复杂的网络结构和用户行为.
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