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Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study) by Rahmattalabi, Aida; Adhikari, Anamika Barman; Vayanos, Phebe; Tambe, Milind; Rice, Eric; Baker, Robin is a scholarly article available to read on EtoBox.

What is Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study) about?

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups, captures the impact of the groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find near-optimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes nea

Author

Rahmattalabi, Aida; Adhikari, Anamika Barman; Vayanos, Phebe; Tambe, Milind; Rice, Eric; Baker, Robin

Published

2019

Language

EN