Parsing social network survey data from hidden populations using stochastic context-free grammars.

ABSTRACT:

Background

Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical m

SUBMITTER: Poon AF 

PROVIDER: S-EPMC2734164 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature