Identifying influential neighbors in social networks and venue affiliations among young MSM: A data science approach to predict HIV infection.

OBJECTIVE Young men who have sex with men (YMSM) bear a disproportionate burden of HIV infection in the United States and their risks of acquiring HIV may be shaped by complex multi-layer social networks. These networks are formed through not only direct contact with social/sex partners but also indirect anonymous contacts encountered when attending social venues. We introduced a new application of a state-of-the-art graph-based deep learning method to predict HIV infection that can identify influential neighbors within these multiple network contexts. DESIGN AND METHODS We used empirical network data among YMSM aged 16-29 years old collected from Houston and Chicago in the U.S. between 2014 and 2016. A computational framework GAT-HIV (Graph Attention Networks for HIV) was proposed to predict HIV infections by identifying influential neighbors within social networks. These networks were formed by multiple relations comprised of social/sex partners and shared venue attendances, and using individual-level variables. Further, GAT-HIV was extended to combine multiple social networks using multi-graph GAT methods. A visualization tool was also developed to highlight influential network members for each individual within the multiple social networks. RESULTS The multi-graph GAT-HIV models obtained average AUC values of 0.776 and 0.824 for Chicago and Houston respectively, performing better than empirical predictive models (e.g. AUCs of random forest: 0.758 and 0.798). GAT-HIV on single networks also delivered promising prediction performances. CONCLUSIONS The proposed methods provide a comprehensive and interpretable framework for graph-based modeling that may inform effective HIV prevention intervention strategies among populations most vulnerable to HIV.