Bayesian learning in undirected graphical models: approximate MCMC algorithms
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A latent mixed membership model for relational data
Proceedings of the 3rd international workshop on Link discovery
Discrete temporal models of social networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Learning dynamic temporal graphs for oil-production equipment monitoring system
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Prediction of Attributes and Links in Temporal Social Networks
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning Non-Stationary Dynamic Bayesian Networks
The Journal of Machine Learning Research
Pervasive sensing to model political opinions in face-to-face networks
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Beyond friendship: modeling user activity graphs on social network-based gifting applications
Proceedings of the 2012 ACM conference on Internet measurement conference
Modeling/predicting the evolution trend of osn-based applications
Proceedings of the 22nd international conference on World Wide Web
Modeling/predicting the evolution trend of osn-based applications
Proceedings of the 22nd international conference on World Wide Web
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. We present a class of hidden temporal exponential random graph models (htERGMs) to study the yet unexplored topic of modeling and recovering temporally rewiring networks from time series of node attributes such as activities of social actors or expression levels of genes. We show that one can reliably infer the latent time-specific topologies of the evolving networks from the observation. We report empirical results on both synthetic data and a Drosophila lifecycle gene expression data set, in comparison with a static counterpart of htERGM.