在信息有限的网络中最大化影响力的个人集中播种策略
Yang Liu1, Xiaoqi Wang2, Xi Wang3,4
1School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University , Xi'an, 710072, China.
Journal of the Royal Society, Interface
|May 8, 2024
概括
本研究引入了一种新方法,用于识别网络中的有影响力的个人,以改善信息传播. 该方法使用受访者提名来找到关键人员,改进现有的种子策略.
科学领域:
- 社交网络分析 社交网络分析
- 信息传播研究 信息传播研究
- 行为经济学是一种行为经济学.
背景情况:
- 同行影响通过交互网络驱动行为,通过准有影响力的个人来放大.
- 目前的种植策略,如一跳式方法,面临不切实际或昂贵的网络数据采集挑战.
- 信息有限的网络阻碍了对干预措施的关键个体的有效准.
研究的目的:
- 提出一个个人集中播种策略,用于在信息有限的网络中识别有影响力的个人.
- 开发一种方法,通过受访者提名推断网络结构,降低数据收集成本.
- 在现实世界的实地研究和干预中提高播种策略的有效性.
主要方法:
- 一个以个人为中心的方法,使用后续问题 (例如",谁有更多的联系/朋友? "). 在这个问题上,我认为.
- 基于从受访者获得最多提名的节点构建一个播种集.
- 在各种实验网络数据集上评估战略的表现.
主要成果:
- 与一跳策略相比,拟议的方法成功地识别了比一跳策略更有影响力的种子.
- 基于提名的推断获得了重要的网络信息,而无需调查额外的个人.
- 该策略在各种网络设置中表现出优于现有方法的性能.
结论:
- 个人集中播种方法在信息有限的网络中有效地准有影响力的个人.
- 这种方法为播种策略的传统网络调查提供了一个实用且具有成本效益的替代方案.
- 拟议的方法有可能在现实场景中提高干预措施的有效性.
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