GroupUs: Smartphone Proximity Data and Human Interaction Type Mining

There is an increasing interest in analyzing social interaction from mobile sensor data, and smart phones are rapidly becoming the most attractive sensing option. We propose a new probabilistic relational model to analyze long-term dynamic social networks created by physical proximity of people. Our model can infer different interaction types from the network, revealing the participants of a given group interaction, and discovering a variety of social contexts. Our analysis is conducted on Bluetooth data sensed with smart phones for over one year on the life of 40 individuals related by professional or personal links. We objectively validate our model by studying its predictive performance, showing a significant advantage over a recently proposed model.