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Substance use and abuse is a significant public health problem in the UnitedStates. Group-based intervention programs offer a promising means of preventingand reducing substance abuse. While effective, unfortunately, inappropriateintervention groups can result in an increase in deviant behaviors amongparticipants, a process known as deviancy training. This paper investigates theproblem of optimizing the social influence related to the deviant behavior viacareful construction of the intervention groups. We propose a Mixed IntegerOptimization formulation that decides on the intervention groups, captures theimpact of the groups on the structure of the social network, and models theimpact of these changes on behavior propagation. In addition, we propose ascalable hybrid meta-heuristic algorithm that combines Mixed IntegerProgramming and Large Neighborhood Search to find near-optimal networkpartitions. Our algorithm is packaged in the form of GUIDE, an AI-baseddecision aid that recommends intervention groups. Being the first quantitativedecision aid of this kind, GUIDE is able to assist practitioners, in particularsocial workers, in three key areas: (a) GUIDE proposes near-optimal solutionsthat are shown, via extensive simulations, to significantly improve over thetraditional qualitative practices for forming intervention groups; (b) GUIDE isable to identify circumstances when an intervention will lead to deviancytraining, thus saving time, money, and effort; (c) GUIDE can evaluate currentstrategies of group formation and discard strategies that will lead to deviancytraining. In developing GUIDE, we are primarily interested in substance useinterventions among homeless youth as a high risk and vulnerable population.GUIDE is developed in collaboration with Urban Peak, a homeless-youth servingorganization in Denver, CO, and is under preparation for deployment.

Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)