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  Submodular Inference of Diffusion Networks from Multiple Trees

Gomez Rodriguez, M., & Schölkopf, B. (2012). Submodular Inference of Diffusion Networks from Multiple Trees. In J. Langford, & J. Pineau (Eds.), 29th International Conference on Machine Learning (ICML 2012) (pp. 489-496). Madison, WI, USA: International Machine Learning Society.

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externe Referenz:
https://icml.cc/2012/papers/281.pdf (Verlagsversion)
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Urheber

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 Urheber:
Gomez Rodriguez, M1, Autor           
Schölkopf, B1, Autor           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

Inhalt

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Schlagwörter: -
 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.

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 Datum: 2012-07
 Publikationsstatus: Erschienen
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 Identifikatoren: BibTex Citekey: GomezRodriguezS2012
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Veranstaltung

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Titel: 29th International Conference on Machine Learning (ICML 2012)
Veranstaltungsort: Edinburgh, UK
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Entscheidung

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Titel: 29th International Conference on Machine Learning (ICML 2012)
Genre der Quelle: Konferenzband
 Urheber:
Langford, J, Herausgeber
Pineau, J, Herausgeber
Affiliations:
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Ort, Verlag, Ausgabe: Madison, WI, USA : International Machine Learning Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 489 - 496 Identifikator: ISBN: 978-1-450-31285-1