Scaling laws of human interaction activity

By Diego Rybski, Sergey V. Buldyrev, Shlomo Havlin, Fredrik Liljeros, Hernán A. Makse. 2009

234 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics, AI for Physical Sciences, Social Sciences & Humanities

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Citations in the corpus

On the origin of long-range correlations in texts (2012, background): “Finally, we believe that our approach may help to understand long-range correlations in any complex system for which an hierarchy of levels can be identified, such as human activities (6) and DNA sequences (9 –11, 39).”
Persistence in eye movement during visual search (2016, methodology): “Taking the mean Fq curve reduces the fluctuations across trials [34].”
Collective Response of Human Populations to Large-Scale Emergencies (2011, unknown): “[3,6,9]), integrating out transient events and noise.”
What's in a crowd? Analysis of face-to-face behavioral networks (2010, background): “On-line interactions occurring between individuals can be monitored by logging instant messaging or email exchange(Eckmann et al., 2004; Kossinets and Watts, 2006; Golder et al., 2007; Leskovec and Horvitz, 2008; Rybski et al., 2009; Malmgren et al., 2009).”