在社交网络中对目标设定选择,有分层的影响力和激活值
Zhecheng Qiang1, Eduardo L Pasiliao2, Qipeng P Zheng1
1Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, Florida USA.
概括
在社交媒体上识别有影响力的用户对于营销和安全至关重要. 本研究提出了有效的算法来选择种子节点,在有限的时间框架内最大限度地影响用户,解决大型网络中的计算挑战.
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
- 信息传播 信息传播
背景情况:
- 社交媒体平台是信息传播的主要道.
- 识别有影响力的用户对于病毒式营销,网络安全和政治应用至关重要.
- 定位有影响力的用户是一个复杂的目标集选择问题.
研究的目的:
- 为了解决分层影响和激活值的目标组选择问题.
- 识别种子节点,在有限的时间框架内最大限度地发挥用户影响.
- 在预算场景中考虑最小的有影响力的种子和最大的影响力.
主要方法:
- 开发了时间索引的整数编程模型,用于种子节点选择.
- 提出了包括图形分区,节点选择和贪算法在内的高效算法.
- 利用广度优先搜索和深度优先搜索进行大规模网络的搜索.
主要成果:
- 有效的算法是为了克服整数编程模型的计算困难而设计的.
- 宽度优先搜索和深度优先搜索贪算法证明对大型网络实例有益.
- 基于节点选择的算法在长尾网络上表现出卓越的性能.
结论:
- 该研究提供了有效的算法解决方案,用于识别社交媒体中具有影响力的用户.
- 高效的算法对于解决大型网络中复杂的影响力最大化问题的解决至关重要.
- 这些发现对优化病毒营销,网络安全和公共安全战略具有重要意义.
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