区分社会传染机制与本地网络视图的区别
Elsa Andres1, Gergely Ódor1,2, Iacopo Iacopini3,4
1Department of Network and Data Science, Central European University, Vienna, 1100 Austria.
概括
本研究展示了如何通过分析个人的行为来识别社会传染过程中的不同采用规则. 它表明,使用本地网络数据可以区分复杂的社会传染机制.
科学领域:
- 社交网络分析分析
- 计算社会科学 计算社会科学
- 行为经济学是一种行为经济学.
背景情况:
- 个人行为采用受到社会互动和外部刺激的影响.
- 共同的模型假设简单或复杂的采用规则,导致社会传染.
- 现实世界的传染过程往往涉及多个并存的采用机制.
研究的目的:
- 确定是否可以从一个本地,自我中心的网络视角中识别社会传染中共存的采用机制.
- 评估在没有全球网络或流程信息的情况下区分不同的采用规则的可行性.
- 通过微观分析,推进对传染机制的理解.
主要方法:
- 制定问题作为分类任务.
- 使用概率方法来分析收养规则.
- 使用随机森林分类器来区分机制.
- 通过合成数据和现实数据进行实验.
主要成果:
- 通过自我中心的网络数据,证明了区分共存的采用机制的能力.
- 在各种实验环境中验证了分类方法的有效性.
- 展示了本地网络视图在理解复杂的传播动态方面的实用性.
结论:
- 在一个单一的社会传染过程中,只使用本地网络信息,就有可能区分多个采用规则.
- 这种自我中心的网络方法为观察传播现象提供了新的视角.
- 这些发现提高了从显微镜角度了解传染机制的理解.
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