Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
Social sensing: obesity, unhealthy eating and exercise in face-to-face networks
WH '10 Wireless Health 2010
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Pervasive sensing to model political opinions in face-to-face networks
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Modeling infection with multi-agent dynamics
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Mining smartphone data to classify life-facets of social relationships
Proceedings of the 2013 conference on Computer supported cooperative work
Working-relationship detection from fitbit sensor data
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Stochastic agent-based simulations of social networks
Proceedings of the 46th Annual Simulation Symposium
Inferring social activities with mobile sensor networks
Proceedings of the 15th ACM on International conference on multimodal interaction
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The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty closely tracking the everyday life of a complete community. We offer evidence that relationships and behavior co-evolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the co-evolution in terms of the rates at which residents visit places and friends.