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Modeling the Diffusion of Preferences on Social Networks

The information diffusion on social networks has been studied for decades. To simplify the diffusion on social networks, most models consider the propagated information or media as single values. Representing media as single values however would not suitable for certain concernful situations such as the voter preference toward the candidates in an election. In such case, the representation would better be lists instead of single values so people can try to alter others’ preference through social inference. This paper studies the diffusion of preference on social networks, which is a novel problem to solve in this direction. First, we propose a preference propagation model that can handle the diffusion of vector-type information instead of only binary or numerical values. Furthermore, we theoretically prove the convergence of diffusion with the proposed model, and that a consensus among strongly connected nodes can eventually be reached with certain conditions. We further extract relevant information from a publicly available bibliography datasets to evaluate the proposed models, while such data can further serve as a benchmark for evaluating future models of the same purpose. Lastly, we exploit the extracted data to demonstrate the usefulness of our model and compare it with other well-known diffusion strategies such as independent cascade, linear threshold, and diffusion rank. We find that our model consistently outperforms other models.