  Studies on social networks have proved that endogenous and exogenous factors
influence dynamics. Two streams of modeling exist on explaining the dynamics of
social networks: 1) models predicting links through network properties, and 2)
models considering the effects of social attributes. In this interdisciplinary
study we work to overcome a number of computational limitations within these
current models. We employ a mean-field model which allows for the construction
of a population-specific socially informed model for predicting links from both
network and social properties in large social networks. The model is tested on
a population of conference coauthorship behavior, considering a number of
parameters from available Web data. We address how large social networks can be
modeled preserving both network and social parameters. We prove that the
mean-field model, using a data-aware approach, allows us to overcome
computational burdens and thus scalability issues in modeling large social
networks in terms of both network and social parameters. Additionally, we
confirm that large social networks evolve through both network and
social-selection decisions; asserting that the dynamics of networks cannot
singly be studied from a single perspective but must consider effects of social
parameters.
