用户实时影响力排名 社交网络的算法 考虑互动性和专题性
Zhaohui Li1, Wenjia Piao1, Zhengyi Sun2
1School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究介绍了UWUSRank,这是一个动态算法,通过考虑网络结构,用户兴趣和时间变化的传播来对社交媒体的用户影响力进行排名. 与现有方法相比,UWUSRank显著提高了排名准确性.
科学领域:
- 社交网络分析 社交网络分析
- 信息科学 信息科学 信息科学
- 计算社会科学 计算社会科学
背景情况:
- 现有的影响评估算法忽视了诸如网络结构,用户利益和时间传播动态等关键因素.
- 传统的方法,如PageRank可能在初始用户影响评估中缺乏客观性.
研究的目的:
- 提出UWUSRank,一种新的动态用户影响力排名算法,它集成了多个属性,用于更准确的社会影响力评估.
- 通过结合用户的内在影响,交互动态和兴趣主题相似性来解决当前算法的局限性.
主要方法:
- 使用活动,身份验证和响应指标确定基本的用户影响.
- 通过分析微博传播网络属性和追随者贡献来量化交互影响.
- 分析用户兴趣主题的相关性,以便在公众论传播过程中实时监控影响.
主要成果:
- 在用户排名中,UWUSRank表现出比TwitterRank更高的9.3%,比PageRank更高的14.2%,比FansRank更高的16.7%.
- 使用真实微博数据进行的实验验证证证了结合用户自身影响力,交互时间和兴趣相似性的有效性.
结论:
- UWUSRank算法为社会影响力排名提供了更全面,更准确的方法.
- 这项研究为用户挖掘,信息传输研究和在社交网络上追踪公众论提供了有价值的见解.
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