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Uncovering the Temporal Dynamics of Diffusion Networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons75510

Gomez Rodriguez,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83792

Balduzzi,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Gomez Rodriguez, M., Balduzzi, D., & Schölkopf, B. (2011). Uncovering the Temporal Dynamics of Diffusion Networks. In 28th International Conference on Machine Learning (ICML 2011) (pp. 561-568). Madison, WI, USA: International Machine Learning Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BB36-3
Zusammenfassung
Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.