Open AccessIdentifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection
Author(s) -
Yang Xiang,
Kayo Fujimoto,
Fang Li,
Qing Wang,
Natascha Del Vecchio,
John A. Schneider,
Degui Zhi,
Cui Tao
Publication year - 2021
Publication title -
aids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.195
H-Index - 216
eISSN - 1473-5571
pISSN - 0269-9370
DOI - 10.1097/qad.0000000000002784
Subject(s) - multigraph , social network (sociolinguistics) , human immunodeficiency virus (hiv) , computer science , graph , social network analysis , men who have sex with men , data science , machine learning , medicine , artificial intelligence , theoretical computer science , social media , immunology , world wide web , syphilis
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.