在多重社交网络中的最大独立集及其应用以影响最大化
Mohammad Mehdi Daliri Khomami1, Alireza Rezvanian2, Mohammad Reza Meybodi1
1Soft Computing Laboratory, Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Scientific reports
|May 10, 2025
概括
本研究引入了在多重社交网络中影响力最大化 (IM) 的新方法. 通过定义和解决最大独立集 (MIS) 问题,它确定了病毒营销的关键传播者.
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
- 营销科学 营销科学
背景情况:
- 影响力最大化对于社交网络中的病毒式营销至关重要.
- 现有的方法往往忽视了用户交互的复杂,多层次的性质.
- 多重网络,不同的协会有不同的层次,更好地代表现实世界的社会结构.
研究的目的:
- 在多重网络中提出影响力最大化的新方法.
- 在这些复杂的网络结构中定义和解决最大独立集合 (MIS) 问题.
- 为了确定针对性活动的最佳初始影响力传播者集.
主要方法:
- 将社交网络建模为具有基于活动的层次的多重网络.
- 定义适用于多重网络结构的最大独立集 (MIS) 问题.
- 开发一个学习自动机框架,以有效地解决MIS问题.
- 在人工和现实世界网络上进行实验,以验证该方法.
主要成果:
- 拟议的MIS方法有效地识别了多重网络中的有影响力的传播者.
- 学习自动机框架证明了MIS的高效计算.
- 实验结果证实了算法在各种网络类型中的性能.
- 建立了多重网络中MIS的理论特性.
结论:
- 最大独立集合 (MIS) 问题为多重网络中的影响最大化提供了一个可行的解决方案.
- 学习自动机框架为这个问题提供了一个有效的计算工具.
- 这项研究促进了对影响力最大化在复杂社会系统中的理解和应用.
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