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Submodular Inference of Diffusion Networks from Multiple Trees

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/persons84193

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

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Zitation

Gomez Rodriguez, M., & Schölkopf, B. (2012). Submodular Inference of Diffusion Networks from Multiple Trees. In 29th International Conference on Machine Learning (ICML 2012) (pp. 1-8). Madison, WI, USA: International Machine Learning Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B6CE-A
Zusammenfassung
Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al. (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.