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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 J. Langford, & J. Pineau (Eds.), 29th International Conference on Machine Learning (ICML 2012) (pp. 313-320). Madison, WI, USA: International Machine Learning Society.

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https://icml.cc/2012/papers/189.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: 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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 Dates: 2012-07
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: GomezRodriguezS2012_2
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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: 313 - 320 Identifier: ISBN: 978-1-4503-1285-1