Network-based intervention strategies to reduce violence among homeless
Violence is a phenomenon that severely impacts homeless youth who are at an increased risk of experiencing it as a result of many contributing factors such as traumatic childhood experiences, involvement in delinquent activities, and exposure to perpetrators due to street tenure. Reducing violence in this population is necessary to ensure that the individuals can safely and successfully exit homelessness and lead a long productive life. Interventions to reduce violence in this population are difficult to implement due to the complex nature of violence. However, a peer-based intervention approach would likely be a worthy approach as previous research has shown that individuals who interact with more violent individuals are more likely to be violent, suggesting a contagious nature of violence. We propose uncertain voter model to represent the complex process of diffusion of violence over a social network that captures uncertainties in links and time over which the diffusion of violence takes place. Assuming this model, we define violence minimization problem where the task is to select a predefined number of individuals for intervention so that the expected number of violent individuals in the network is minimized over a given time frame. We also extend the problem to a probabilistic setting, where the success probability of converting an individual into nonviolent is a function of the number of “units” of intervention performed on them. We provide algorithms for finding the optimal intervention strategies for both scenarios. We demonstrate that our algorithms perform significantly better than interventions based on popular centrality measures in terms of reducing violence. Finally, we use our optimal algorithm for probabilistic intervention to recruit peers in a homeless youth shelter as a pilot study. Our surveys before and after the intervention show a significant reduction in violence.
This work is supported by US National Science Foundation under EAGER Award Number 1637372.
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