Trade-offs in social and behavioral modeling in mobile networks

Yaniv Altshuler, Michael Fire, Nadav Aharony, Zeev Volkovich, Yuval Elovici, Alex Pentl,

Social Computing, Behavioral-Cultural Modeling and Prediction: 6th …, 2013

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 ” …