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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.

Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascade scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most “influential” nodes in the network. The majority of prior work, however, focuses on unsigned networks where individuals adopt the opinion of their neighbors with certain probability. In real life, relationships between individuals can be positive (e.g., friend of relationship) or negative (e.g., connection between “foes”). According to social theory, people tend to have similar opinions to their friends but opposite of their foes. We study the problem of competing cascades on signed networks, which has been relatively unexplored. Particularly, we study the progressive propagation of two competing cascades in a signed network under the Independent Cascade Model and Generalized Linear Threshold Model and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We validate the quality of our approximation on several synthetic graphs. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem. We allow the seed-set to be initialized with populations of both cascades with the end goal of maximizing the spread of one cascade. We validate our approach on several large-scale real-world and synthetic networks. Our experiments demonstrate that our influence maximization heuristic significantly outperforms state-of-the-art methods, particularly when the network is dominated by distrust relationships.

The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. In an earlier work, we proposed a unified model which provides an approximate analytical solution to the problem of predicting probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We build upon our unified model to develop an efficient method for influence maximization. Unlike most approaches, we assume that diffusion spreads not only via the edges of the underlying network, but also through temporal functions of external out-of-network processes. We empirically evaluate our approach and compare it against state-of-the-art approaches on real-world large-scale networks. The evaluation demonstrates that our method has significant performance gains over widely used seed-set selection algorithms.