Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. These constraints provide a better proxy for social influence, showing a stronger correlation to the probability of influence as well as the ability to predict influence.
@article{arxiv.1705.02399,
title = {Temporal Analysis of Influence to Predict Users' Adoption in Online Social Networks},
author = {Ericsson Marin and Ruocheng Guo and Paulo Shakarian},
journal= {arXiv preprint arXiv:1705.02399},
year = {2017}
}
Comments
6 pages, 2 figures, 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2017). July 5 - 8, 2017