Structure of a large social network

We study a social network consisting of over $10^4$ individuals, with a degree distribution exhibiting two power scaling regimes separated by a critical degree $k_{\rm crit}$, and a power law relation between degree and local clustering. We introduce a growing random model based on a local interaction mechanism that reproduces all of the observed scaling features and their exponents. Our results lend strong support to the idea that several very different networks are simultenously present in the human social network, and these need to be taken into account for successful modeling.