Using social networks to raise HIV awareness among homeless youth

Many homeless shelters conduct interventions to raise awareness about HIV (human immunodeficiency virus) among homeless youth. Due to human and financial resource shortages, these shelters need to choose intervention attendees strategically, in order to maximize awareness through the homeless youth social network. In this work, we propose HEALER (hierarchical ensembling based agent which plans for effective reduction in HIV spread), an agent that recommends sequential intervention plans for use by homeless shelters. HEALER's sequential plans (built using knowledge of homeless youth social networks) select intervention participants strategically to maximize influence spread, by solving POMDPs (partially observable Markov decision process) on social networks using heuristic ensemble methods. This paper explores the motivations behind HEALER’s design, and analyzes HEALER’s performance in simulations on real-world networks. First, we provide a theoretical analysis of the DIME (dynamic influence maximization under uncertainty) problem, the main computational problem that HEALER solves. HEALER relies on heuristic methods for solving the DIME problem due to its computational hardness. Second, we explain why heuristics used inside HEALER work well on real-world networks. Third, we present results comparing HEALER to baseline algorithms augmented by HEALER’s heuristics. HEALER is currently being tested in real-world pilot studies with homeless youth in Los Angeles.