Origins of power-law degree distribution in the heterogeneity of human activity in social networks

Abstract

The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a "maximum entropy attachment" model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.

23 pages, 5 figures

References in corpus (4)

• Power-law distributions in empirical data
• Statistical physics of social dynamics
• Activity driven modeling of time varying networks
• On the Identifiability of the Post-Nonlinear Causal Model

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