Influence propagation in social networks has recently received large interest. In fact, the understanding of how influence propagates among subjects in a social network opens the way to a growing number of applications. Many efforts have been made to quantitatively measure the influence probability between pairs of subjects. Existing approaches have two main drawbacks: (i ) they assume that the influence probabilities are independent of each other, and (i i ) they do not consider the actions not performed by the subject (but performed by her/his friends) to learn these probabilities. In this paper, we propose to address these limitations by employing a deep learning approach. We introduce a Deep Neural Network (DNN) framework that has the capability for both modeling social influence and for predicting human behavior. To empirically validate the proposed framework, we conduct experiments on a real-life (offline) dataset of an Event-Based Social Network (EBSN). Results indicate that our approach outperforms existing solutions, by efficiently resolving the limitations previously described.
CITATION STYLE
Luceri, L., Braun, T., & Giordano, S. (2018). Social influence (Deep) learning for human behavior prediction. In Springer Proceedings in Complexity (Vol. 0, pp. 261–269). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-319-73198-8_22
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