  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.
