A Re-examination of Connectivity Trends via Exponential Random Graph Modeling in Two IDU Risk Networks
Abstract: Patterns of risk in injecting drug user (IDU) networks—including co-use, equipment sharing, and sex in the context of drug use—have been a key focus of network approaches to HIV and other disease transmission histories. New network modeling techniques allow for a re-examination of these patterns with greater statistical accuracy and the comparative weighting of model elements. This paper describes the results of a re-examination of network data from the SFHR and P90 data sets using Exponential Random Graph Modeling (ERGM). ERGM allows researchers to produce “regression-like” models to describe connection tendencies that consider both personal attributes (such as age, gender, race/ethnicity, and injection history) and network structural factors (such as transitivity—the tendency of an individual to form a network tie with the partner of a current partner). Similar to ordinary statistical modeling, ERGM network models provide model coefficients that demonstrate the relative weighting of such factors as they contribute to the formation of the networks in question, along with error estimation statistics. Cross-network comparison of the relative importance of model coefficients is also possible. These findings contribute to a more clear understanding of the “logic” of network connectivity among IDU networks.
Keywords: Injector Networks, ERGM, HIV Transmission, Network Modeling
This project was supported by NIH/NIDA Challenge Grant 1RC1DA028476-01/02 awarded to the CUNY Research Foundation and John Jay College, CUNY. Initial funding for a pilot version of this project was provided by the NSF Office of Behavioral, Social, and Economic Sciences, Anthropology Program Grant BCS-0752680.