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A new algorithm for the influence maximization problem in dynamic networks or traffic sensor networks

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Abstract

Influence spread is one of the key problems in complex networks, and the results of influence maximization problem (IMP) based on dynamic networks are less. In this paper, we discuss the dynamic IMP and describe the dynamic independent cascade model (DICM) and the dynamic linear threshold model (DLTM). We also conclude that IMP based on DICM and DLTM is NP-Hard. To solve the IMP, we present an improved greedy algorithm that is validated based on four datasets with different sizes. Our findings indicate that, compared with the HT algorithm, the size of the influence spread of our algorithm has an obvious advantage, and time efficiency is better than that of the HT algorithm.

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Acknowledgments

This work is supported by National Social Science Foundation of China (No. 11BF- X125), PuJiang Talent Project, Peak of law subject construction and Public Security Discipline Construction Foundation.

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Authors and Affiliations

  1. Department of Computer Science, East China University of Political Science and Law, Shanghai, 201620, China

    Xue-Guang Wang

  2. Department of Computer Science, University College London (UCL), London, WC1E 6BT, UK

    Xue-Guang Wang

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  1. Xue-Guang Wang
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Correspondence to Xue-Guang Wang.

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Wang, XG. A new algorithm for the influence maximization problem in dynamic networks or traffic sensor networks. Multimed Tools Appl 75, 4833–4844 (2016). https://doi.org/10.1007/s11042-016-3266-9

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  • DOI: https://doi.org/10.1007/s11042-016-3266-9

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