The probability distribution of number of ties of an individual in a social network follows a scalefree 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.
Muchnik et al. REF demonstrated that heavy-tailed degree distributions in networks are causally determined by similarly skewed distributions of human activity.
Origins of power-law degree distribution in the heterogeneity of human activity in social networks
Scientific Reports, 3, 1783 (2013)
Mathematics Medicine Physics Computer Science