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The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

ICNC-FSKD 2019: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery pp 214–223Cite as

Optimal Identification of Multiple Diffusion Sources in Complex Networks with Partial Observations

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1074))

Abstract

Source localization is a typical inverse problem in complex networks, which is widely used in disease outbreak, rumor propagation and pollutants spread. In this paper, we propose that, based on network topology and the times at which the diffusion reached partial nodes, it is easy to identify the source. The results show that in six different networks, although the number of observers is small, the precision of source localization can be high. Precision increases with network size increasing and source number decreasing. Furthermore, our method makes the sources localization precision very robust, not only with the condition of three different given observers selection strategies, but with three various intensity noise on the diffusion path.

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Acknowledgments

This work is partially supported by the National Key R&D Program of China (Grant No. 2017YCF1200301), the Postgraduate Innovation Fund of Hunan Province (Grant No. CX2015B010), and the Postgraduate Innovation Fund of the National University of Defense Technology (Grant No. B150203).

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Authors and Affiliations

  1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, 410073, China

    Xiang Li, Xiaojie Wang, Chengli Zhao, Xue Zhang & Dongyun Yi

  2. State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China

    Dongyun Yi

Authors
  1. Xiang Li
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  2. Xiaojie Wang
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  3. Chengli Zhao
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  4. Xue Zhang
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  5. Dongyun Yi
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Corresponding author

Correspondence to Chengli Zhao .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Li, X., Wang, X., Zhao, C., Zhang, X., Yi, D. (2020). Optimal Identification of Multiple Diffusion Sources in Complex Networks with Partial Observations. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_23

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