基于当地社区和用户反应信息,预测社交网络中的有影响力的节点
Rohollah Rashidi1, Farsad Zamani Boroujeni2, MohammadReza Soltanaghaei1
1Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
Scientific reports
|July 9, 2024
概括
这项研究引入了一种新的方法,通过结合用户特征和反应来识别大型社交网络中的有影响力的节点. 该方法使用社区检测来简化分析,提高网络管理应用程序的准确性和处理时间.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 网络科学 网络科学
背景情况:
- 识别有影响力的节点对于管理大型社交网络至关重要.
- 应用包括谣言检测,定向广告,交通预测和疾病传播建模.
- 现有的方法在大型网络的可扩展性和处理能力方面面临挑战.
研究的目的:
- 提出一种新的,有效的方法来识别社交网络中的有影响力的节点.
- 利用社会特征和用户反应数据来改进影响力检测.
- 通过社区检测来减少计算复杂性.
主要方法:
- 结合了用户的社会特征和反应信息.
- 使用社区检测来分割网络,简化识别问题.
- 使用链接预测来预测未来的连接.
- 应用基于中心性和影响领域标准的代策略.
- 评估社区和网络层面的绩效.
主要成果:
- 拟议的方法在检测有影响力的节点方面表现出卓越的性能.
- 与以前的方法相比,实现了更高的准确性和回忆.
- 显著改善处理时间,提高大规模网络的效率.
结论:
- 这种新的方法通过整合各种用户数据和网络结构,有效地识别有影响力的节点.
- 社区检测和联系预测提高了影响分析的可扩展性和准确性.
- 该方法为各种网络管理和分析任务提供了可靠的解决方案.
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