Abstract

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 (ii) 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 …

Metadata

publication

Proceedings of the 9th Conference on Complex Networks - CompleNet' 2018, 2018

year

2018

publication date

2018

authors

Luca Luceri, Torsten Braun, Silvia Giordano

link

https://link.springer.com/chapter/10.1007/978-3-319-73198-8_22

resource_link

https://arxiv.org/pdf/1801.09471

conference

Proceedings of the 9th Conference on Complex Networks - CompleNet' 2018