@inproceedings{61d04a8f47794e0199b1bbf2a45e33f3,
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",
note = "Funding Information: Some of the authors of this paper are supported by CNPq-Brazil, AFOSR Young Investigator Program (YIP) grant FA9550-15-1-0159, ARO grant W911NF-15-1-0282, and the DoD Minerva program.; 10th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2017 ; Conference date: 05-07-2017 Through 08-07-2017",
year = "2017",
doi = "10.1007/978-3-319-60240-0_31",
language = "English (US)",
isbn = "9783319602394",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "254--261",
editor = "Nathaniel Osgood and Dongwon Lee and Robert Thomson and Yu-Ru Lin",
booktitle = "Social, Cultural, and Behavioral Modeling - 10th International Conference, SBP-BRiMS 2017, Proceedings",
}