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  Influence Maximization in Continuous Time Diffusion Networks

Gomez Rodriguez, M., & Schölkopf, B. (2012). Influence Maximization in Continuous Time Diffusion Networks. In 29th International Conference on Machine Learning (ICML 2012) (pp. 1-8). Madison, WI, USA: International Machine Learning Society.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-B6C4-D Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-B6C5-B
Genre: Konferenzbeitrag

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 Urheber:
Gomez Rodriguez, M1, Autor           
Schölkopf, B1, Autor           
Langford J. Pineau, J., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends dramatically on the underlying temporal dynamics of the network. However, this still remains largely unexplored to date. To this end, given a network and its temporal dynamics, we first describe how continuous time Markov chains allow us to analytically compute the average total number of nodes reached by a diffusion process starting in a set of source nodes. We then show that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and develop an efficient approximation algorithm with provable near-optimal performance. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by at least ~20 and is robust across different network topologies.

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Sprache(n):
 Datum: 2012-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: ISBN: 978-1-450-31285-1
URI: http://icml.cc/2012/
BibTex Citekey: GomezRodriguezS2012_2
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Titel: 29th International Conference on Machine Learning (ICML 2012)
Veranstaltungsort: Edinburgh, UK
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Titel: 29th International Conference on Machine Learning (ICML 2012)
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Madison, WI, USA : International Machine Learning Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1 - 8 Identifikator: -