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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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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-B6CE-A Version Permalink: https://hdl.handle.net/21.11116/0000-0001-1BBF-B
Genre: Conference Paper

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https://icml.cc/2012/papers/281.pdf (Publisher version)
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 Creators:
Gomez Rodriguez, M1, Author           
Schölkopf, B1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: 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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 Dates: 2012-07
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: GomezRodriguezS2012
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Title: 29th International Conference on Machine Learning (ICML 2012)
Place of Event: Edinburgh, UK
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Title: 29th International Conference on Machine Learning (ICML 2012)
Source Genre: Proceedings
 Creator(s):
Langford, J, Editor
Pineau, J, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 489 - 496 Identifier: ISBN: 978-1-450-31285-1