Joint Scaling Theory of Human Dynamics and Network Science
Year: 2012
Type: preprint
Abstract: The increasing availability of large-scale data on human behavior hascatalyzed simultaneous advances in network theory, capturing the scaling
properties of the interactions between a large number of individuals, and human
dynamics, quantifying the temporal characteristics of human activity patterns.
These two areas remain disjoint, however, traditionally each pursuing as a
separate modeling framework. Here we establish the first formal link between
these two areas by showing that the exponents characterizing the degree and
link weight distribution in social networks can be expressed in terms of the
dynamical exponents characterizing human activity patterns. We test the
validity of these theoretical predictions on datasets capturing various facets
of human interactions, from mobile calls to tweets. We find evidence of a
universal measure, that links networks and human dynamics, but whose value is
independent of the means of communication, capturing a fundamental property of
human activity.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2404321141