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

ABSTRACT:

Objective

Young MSM (YMSM) bear a disproportionate burden of HIV infection in the United States and their risks of acquiring HIV may be shaped by complex multilayer 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 United States between 2014 and 2016. A computational framework GAT-HIV (Graph Attention Networks for HIV) was proposed to pre

SUBMITTER: Xiang Y 

PROVIDER: S-EPMC8058230 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature