Many homeless shelters conduct interventions to raise awareness about HIV (human immunodeficiency virus) infection among homeless youth. Because of human and financial resource shortages, these shelters need to choose intervention attendees strategically, 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) strategically select intervention participants to maximize influence spread, by solving POMDPs (partially observable Markov decision processes) on social networks using heuristic ensemble methods. In this paper, we explore the motivations behind the design of HEALER and analyze the performance of HEALER 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 within HEALER work well on real-world networks. Third, we present results comparing HEALER to baseline algorithms augmented by the heuristics of HEALER. HEALER is currently being tested in real-world pilot studies with homeless youth in Los Angeles, California.
All Science Journal Classification (ASJC) codes
- Computer Science(all)