Effect of observation time on source identification of diffusion in complex networks

doi:10.1088/1674-1056/ac5985

中国物理B ›› 2022, Vol. 31 ›› Issue (7): 70203-070203.doi: 10.1088/1674-1056/ac5985

Effect of observation time on source identification of diffusion in complex networks

Chaoyi Shi(史朝义)¹, Qi Zhang(张琦)², and Tianguang Chu(楚天广)^1,†

1 College of Engineering, Peking University, Beijing 100871, China;
2 School of Information Technology and Management, University of International Business and Economics, Beijing 100105, China

收稿日期:2021-12-29 修回日期:2022-01-30 接受日期:2022-03-02 出版日期:2022-06-09 发布日期:2022-06-13
通讯作者: Tianguang Chu E-mail:chutg@pku.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61673027 and 62106047), the Beijing Social Science Foundation (Grant No. 21GLC042), and the Humanity and Social Science Youth foundation of Ministry of Education, China (Grant No. 20YJCZH228).

Effect of observation time on source identification of diffusion in complex networks

Chaoyi Shi(史朝义)¹, Qi Zhang(张琦)², and Tianguang Chu(楚天广)^1,†

1 College of Engineering, Peking University, Beijing 100871, China;
2 School of Information Technology and Management, University of International Business and Economics, Beijing 100105, China

Received:2021-12-29 Revised:2022-01-30 Accepted:2022-03-02 Online:2022-06-09 Published:2022-06-13
Contact: Tianguang Chu E-mail:chutg@pku.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61673027 and 62106047), the Beijing Social Science Foundation (Grant No. 21GLC042), and the Humanity and Social Science Youth foundation of Ministry of Education, China (Grant No. 20YJCZH228).

摘要/Abstract

摘要： This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infected-recovered diffusion process in a network with snapshot of partial nodes. We formulate the source identification problem as a maximum likelihood (ML) estimator and develop a statistical inference method based on Monte Carlo simulation (MCS) to estimate the source location and the initial time of diffusion. Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.

关键词: complex network, source identification, statistical inference, partial observation

Abstract: This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infected-recovered diffusion process in a network with snapshot of partial nodes. We formulate the source identification problem as a maximum likelihood (ML) estimator and develop a statistical inference method based on Monte Carlo simulation (MCS) to estimate the source location and the initial time of diffusion. Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.

Key words: complex network, source identification, statistical inference, partial observation

中图分类号: (Applications of Monte Carlo methods)

02.70.Uu

87.23.Ge (Dynamics of social systems) 89.75.-k (Complex systems)

Chaoyi Shi(史朝义), Qi Zhang(张琦), and Tianguang Chu(楚天广). Effect of observation time on source identification of diffusion in complex networks[J]. 中国物理B, 2022, 31(7): 70203-070203.

Chaoyi Shi(史朝义), Qi Zhang(张琦), and Tianguang Chu(楚天广). Effect of observation time on source identification of diffusion in complex networks[J]. Chin. Phys. B, 2022, 31(7): 70203-070203.

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Effect of observation time on source identification of diffusion in complex networks

Effect of observation time on source identification of diffusion in complex networks

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