大数据时代复杂社会传染模型

Xuyang Ding, Zhangjian Wu, Wantao Chen, Y. Liu, Ying Xie, Shimin Cai
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引用次数: 4

摘要

在大数据时代,个人被各种各样的社交媒体包围着,比如Facebook、Twitter、微博。这些社交媒体每天都会产生大量的信息,并支持各种社会传染。然而,由于大数据的原因,这些社会传染的动态和机制仍然是模糊和未揭示的。本文提出了一种新的非马尔可夫社会传染模型来研究大数据环境下的行为传播,即一小部分全局个体可以将行为信息传递给每一个易感个体,而剩余的局部个体只能将行为信息传递给邻居。通过大量的数值模拟,我们发现全局个体显著地促进了行为的传播,降低了关键信息的传递概率。此外,我们注意到社会网络的程度异质性并没有从质的上改变这一现象。我们的研究结果可能为预测和控制社会传染提供一些启示。进一步,该模型可应用于大数据时代应急管理的真实仿真平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling complex social contagions in big data era
In big data era, individuals are surrounded by various kinds of social medium, such as Facebook, Twitter and Microblog. These social media produce vast information every day and support diverse social contagions. However, the dynamics and mechanisms of these social contagions are still obscure and unrevealed because of the big data. In this paper, we propose a novel non-Markovian social contagion model to study behavior spreading under the environment of big data, in which a fraction of global individuals can transmit the behavior information to every susceptible individual, and the remaining local individuals can only transmit the behavior information to neighbors. Through extensive numerical simulations, we find that the global individuals markedly promote the behavior spreading and decrease the critical information transmission probability. In addition, we note that the degree heterogeneity of social network does not change the phenomena qualitatively. Our results may shed some lights in predicting and controlling social contagions. In further, the proposed model may be applied in real simulation platforms for emergency management in big data era.
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