@inproceedings{9f3e636d983144f7b8837710a526dbf6,
title = "Temporal analysis of influence to predict users{\textquoteright} adoption in online social networks",
abstract = "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.",
author = "Ericsson Marin and Ruocheng Guo and Paulo Shakarian",
year = "2017",
doi = "10.1007/978-3-319-60240-0_31",
language = "English",
isbn = "978-3-319-60239-4",
series = "Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)",
publisher = "Springer Nature",
pages = "254--261",
editor = "Dongwon Lee and Yu-Ru Lin and Nathaniel Osgood and Robert Thomson",
booktitle = "Social, Cultural, and Behavioral Modeling",
note = "10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 ; Conference date: 05-07-2017 Through 08-07-2017",
}