报 告 人：周昌松 教授（香港浸会大学）

报告时间：2018年8月9日 （星期四），上午10:30

报告地点：威廉希尔五层会议室（致知楼3514）

报告摘要：

Interacting human activities underlie all social and economic phenomena, but quantitative understanding of regular patterns and mechanism is very challenging and still rare. Electrical communication and self-organized online collaborative activities with precise record of event timing provide unprecedented opportunity. In our previous work [1], we analyzed the interevent time of Short Message communication. We found that observed human actions are the result of the interplay of three basic ingredients: Poisson initiation of tasks and decision making for task execution in individual humans as well as interaction among individuals. This interplay leads to new types of interevent time distribution, neither completely Poisson nor power-law, but a bimodal combination of them. We show that the events can be separated into independent bursts which are generated by frequent mutual interactions in short times following random initiations of communications in longer times by the individuals. We introduced a minimal model of two interacting priority queues incorporating the three basic ingredients, which fits well the distributions using the parameters extracted from the empirical data.

In our recent work, we extended the analysis and modeling to unfold large-scale on-line collaborative human dynamics [2]. Our empirical analysis of the history of millions of updates in Wikipedia showed a universal double power- law distribution of time intervals between consecutive updates of an article. Based on [1], we then proposed a generic model to unfold collaborative human activities into three modules: (i) individual behavior characterized by Poissonian initiation of an action, (ii) human interaction captured by a cascading response to previous actions with a power- law waiting time, and (iii) population growth due to the increasing number of interacting individuals. This unfolding allows us to obtain analytical formula that is fully supported by the universal patterns in empirical data. In particular, interacting dynamics with constant initiation rate will lead to the bimodal distribution as in [1], and the inhomogeneous initiation rates due to exponential growth of the interacting populations will generate universal double power law. 

Our analysis and modeling approaches reveal “simplicity” beyond complex interacting human activities. It will be interesting to further study how the individual differences and personal traits play a role in human communication and collaboration dynamics.  

[1] Y. Wu, C.S. Zhou, J. Xiao, J. Kurths and H.J. Schellnhuber, “Evidence for a bimodal distribution in human communication”, Proc Natl Acad Sci USA, 107, 18803 (2010).

[2] Y.L. Yilong Zha, T. Zhou and C.S. Zhou, “Unfolding large-scale on-line collaborative human dynamics”, Proc Natl Acad Sci USA 113, 14627 (2016).   

报告人简介：周昌松, 物理学博士，香港浸会大学物理系教授，浸会大学非线性研究中心及北京-香港-新加坡非线性复杂系统联合中心主任。1992年获南开大学物理学士, 1997年获南开大学物理博士，1997-2007年在新加坡、 香港、 德国等地从事访问研究, 是洪堡基金获得者。 2007年加入香港浸会大学物理系， 2011年获浸会大学“杰出青年研究者董事长奖”。周昌松博士致力于复杂系统动力学基础研究及其应用，特别是网络的复杂联结结构与体系的动态行为的关系和相互作用。近几年一直与国际国内系统和认知神经科学家紧密合作，把这些理论进展应用到大脑的复杂联结结构和活动以及认知功能及障碍的分析和建模等方面研究中。在国际交叉学术刊物PNAS，PRL, Physics Reports, PLoS Computational Biology等发表论文120余篇，SCI引用7500余次，H-因子为35 (Google Scholar引用12000余次，H-因子为44)。任 Scientific Reports 编委, PLoS One 学术编辑，及多种国际期刊常任审稿人。