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Time Varying Graphical Models: Reverse Engineering and Analyzing Rewiring Networks

Time Varying Graphical Models: Reverse Engineering and Analyzing Rewiring Networks

This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. A plausible representation of the relational information among entities in dynamic systems such as a social community or a living cell is a stochastic network that is topologically rewiring and semantically evolving over time. While there is a rich literature in modeling static or temporally invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present a new formalism for modeling network evolution over time based on time-evolving probabilistic graphical models, such as TV-GGM, TV-MRF, and TV-DBN, and several new algorithms... Show More

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