Scaling laws of human interaction activity
Scaling laws of human interaction activity
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DOI：
10.1073/pnas.0902667106
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发表时间：
2009-08-04
影响因子：
11.1
通讯作者：
Makse, Hernan A.
中科院分区：
文献类型：
--
作者：
Rybski, Diego;Buldyrev, Sergey V.;Makse, Hernan A.
尽管在我们当代的科技社会中人们依赖于交流，但我们对人类交流行为的基本规律的理解仍然知之甚少。在此，我们研究了两个社交网络社区中的交流模式，以寻找人类互动活动中的统计规律。这项研究表明，人类交流网络动态地遵循标度律，这也可能解释所观察到的经济增长趋势。具体而言，我们确定了社会活动的吉布拉特定律的一个广义版本，它表现为成员发送的消息数量的波动与其活动水平之间的标度律。吉布拉特定律对于理解经济增长模式至关重要，但对于其起源却没有一个基本的通用原理。我们将这种标度律归因于人类活动中的长期相关性模式，令人惊讶的是，这种相关性从几天到超过1年的可用数据的整个时间段都存在。此外，我们提供了一个数学框架，将吉布拉特定律的广义版本与长期相关的动态联系起来，这表明相同的潜在机制可能是经济学中吉布拉特定律的根源，从大公司、研发支出、国家的国内生产总值到城市人口增长。这些发现对于设计交流网络以及理解交流起作用的社会系统（如经济市场和政治系统）的动态也具有重要意义。
Even though people in our contemporary technological society are depending on communication, our understanding of the underlying laws of human communicational behavior continues to be poorly understood. Here we investigate the communication patterns in 2 social Internet communities in search of statistical laws in human interaction activity. This research reveals that human communication networks dynamically follow scaling laws that may also explain the observed trends in economic growth. Specifically, we identify a generalized version of Gibrat's law of social activity expressed as a scaling law between the fluctuations in the number of messages sent by members and their level of activity. Gibrat's law has been essential in understanding economic growth patterns, yet without an underlying general principle for its origin. We attribute this scaling law to long-term correlation patterns in human activity, which surprisingly span from days to the entire period of the available data of more than 1 year. Further, we provide a mathematical framework that relates the generalized version of Gibrat's law to the long-term correlated dynamics, which suggests that the same underlying mechanism could be the source of Gibrat's law in economics, ranging from large firms, research and development expenditures, gross domestic product of countries, to city population growth. These findings are also of importance for designing communication networks and for the understanding of the dynamics of social systems in which communication plays a role, such as economic markets and political systems.