  The ability to sample random networks that can accurately represent real
social contact networks is essential to the study of viral epidemics. The
Barab\'asi-Albert model and its extensions attempt to capture reality by
generating networks with power-law degree distributions, but properties of the
resulting distributions (e.g. minimum, average, and maximum degree) are often
unrealistic of the social contacts the models attempt to capture. I propose a
novel extension of the Barab\'asi-Albert model, which I call the
"dual-Barab\'asi-Albert" (DBA) model, that attempts to better capture these
properties of real networks of social contact.
