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

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