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Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

Identifying Sources of Random Walk-Based Epidemic Spreading in Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_32,
        author={Bo Qin and Cunlai Pu},
        title={Identifying Sources of Random Walk-Based Epidemic Spreading in Networks},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={Source identification Random walk Maximum likelihood (ML) Network heterogeneity},
        doi={10.1007/978-3-030-32388-2_32}
    }
    
  • Bo Qin
    Cunlai Pu
    Year: 2019
    Identifying Sources of Random Walk-Based Epidemic Spreading in Networks
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_32
Bo Qin1, Cunlai Pu1,*
  • 1: Nanjing University of Science and Technology
*Contact email: pucunlai@njust.edu.cn

Abstract

Identifying the sources of epidemic spreading is of critical importance to epidemic control and network immunization. However, the task of source identification is very challenging, since in real situations the dynamics of the spreading process is usually not clear. In this paper, we formulate the multiple source epidemic spreading process as the multiple random walks, which is a theoretical model applicable to various spreading processes. Considering the different influence of distinct epidemic sources on the observed infection graph, we derive the maximum likelihood estimator of the multiple source identification problem. Simulation results on real-world networks and network models, such as the Price model and Erdös-Rényi (ER) model, demonstrate the efficiency of our estimator. Furthermore, we find that the efficiency of our estimator increases with the enhancement of network sparsity and heterogeneity.

Keywords
Source identification Random walk Maximum likelihood (ML) Network heterogeneity
Published
2019-10-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-32388-2_32
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