2011 IEEE 12th International Conference on Mobile Data Management (2011)
June 6, 2011 to June 9, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MDM.2011.18
By exploiting built-in sensors, mobile smart phone have become attractive options for large-scale sensing of human behavior as well as social interaction. In this paper, we present a new probabilistic model to analyze longitudinal dynamic social networks created by the physical proximity of people sensed continuously by the phone Bluetooth sensors. A new probabilistic model is proposed in order to jointly infer emergent grouping modes of the community together with their temporal context. We present experimental results on a Bluetooth proximity network sensed with mobile smart-phones over 9 months of continuous real-life, and show the effectiveness of our method.
T. M. Do and D. Gatica-Perez, "Contextual Grouping: Discovering Real-Life Interaction Types from Longitudinal Bluetooth Data," 2011 12th IEEE International Conference on Mobile Data Management (MDM 2011)(MDM), Lulea, 2011, pp. 256-265.