Trade-Offs in Social and Behavioral Modeling in Mobile Networks
Name
SBP13-Learning.pdf
Description
Accepted version
Size
790.33 KB
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Unknown
Checksum (MD5)
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Author(s)
Altshuler, Yaniv
Fire, Michael
Aharony, Nadav
Volkovich, Zeev
Elovici, Yuval
Pentland, Alex
Date Issued
2013
Publisher
Springer Berlin Heidelberg
Citation
Altshuler, Yaniv, Fire, Michael, Aharony, Nadav, Volkovich, Zeev, Elovici, Yuval et al. 2013. "Trade-Offs in Social and Behavioral Modeling in Mobile Networks."
Version
Author's final manuscript
Abstract
Mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today's smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and behavior prediction. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones. In particular, we look at the dynamic learning process over time with various sizes of sampling groups and examine the interplay between these two parameters. We validate our model using extensive simulations carried out using the "Friends and Family" dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year and is one of the most comprehensive mobile phone datasets gathered in academia to date. © 2013 Springer-Verlag.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-642-37210-0_45
