Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sensing and modeling human networks
Sensing and modeling human networks
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Towards the automated social analysis of situated speech data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining social relationship types in an organization using communication patterns
Proceedings of the 2013 conference on Computer supported cooperative work
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In this paper, we present a novel probabilistic framework for recovering global, latent social network structure from local, noisy observations. We extend curved exponential random graph models to include two types of variables: hidden variables that capture the structure of the network and observational variables that capture the behavior between actors in the network. We develop a novel combination of informative and intuitive conversational (local) and structural (global) features to specify our model. The model learns, in an unsupervised manner, the relationship between observable behavior and hidden social structure while simultaneously learning properties of the latent structure itself. We present empirical results on both synthetic data and a real world dataset of face-to-face conversations collected from 24 individuals using wearable sensors over the course of 6 months.