Social Influence (Deep) Learning for Human Behavior Prediction

TL;DR

This paper introduces a deep learning framework for modeling social influence and predicting human behavior, overcoming limitations of previous methods by capturing interdependent influence probabilities and considering unperformed actions.

Contribution

The paper presents a novel deep neural network approach that models social influence more accurately and predicts human behavior in social networks, addressing key limitations of prior models.

Findings

Outperforms existing influence modeling methods

Effectively captures interdependent influence probabilities

Accurately predicts human behavior in social networks

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 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.