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ICML
2007
IEEE

Recovering temporally rewiring networks: a model-based approach

10 years 2 months ago
Recovering temporally rewiring networks: a model-based approach
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 timespecific 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 ...
Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
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