Modeling for heterogeneous multi-stage information propagation networks and maximizing information

doi:10.1088/1674-1056/28/2/028701

中国物理B ›› 2019, Vol. 28 ›› Issue (2): 28701-028701.doi: 10.1088/1674-1056/28/2/028701

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇

Modeling for heterogeneous multi-stage information propagation networks and maximizing information

Ren-Jie Mei(梅人杰), Li Ding(丁李), Xu-Ming An(安栩明), Ping Hu(胡萍)

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

收稿日期:2018-10-05 修回日期:2018-11-19 出版日期:2019-02-05 发布日期:2019-02-05
通讯作者: Li Ding E-mail:liding@whu.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 61873194).

Modeling for heterogeneous multi-stage information propagation networks and maximizing information

Ren-Jie Mei(梅人杰), Li Ding(丁李), Xu-Ming An(安栩明), Ping Hu(胡萍)

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Received:2018-10-05 Revised:2018-11-19 Online:2019-02-05 Published:2019-02-05
Contact: Li Ding E-mail:liding@whu.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 61873194).

摘要/Abstract

摘要： In this paper, we propose a heterogeneous multi-stage model to study the effect of social reinforcement on information propagation. Both heterogeneity of network components and the heterogeneity of individual reinforcement thresholds are considered. An information outbreak condition is derived, according to which the outbreak scale and individual density of each state under specific propagation parameters can be deduced. Monte Carlo experiments are conducted in Facebook networks to demonstrate the outbreak condition, and we find that social reinforcement effects generally inhibit the propagation of information though it contributes to the emergence of certain hot spots simultaneously. Additionally, by applying Pontryagin's Maximum Principle, we derive the optimal control strategy in the case of limited control resources to maximize the information propagation. Then the forward-backward sweep method is utilized to verify its performance with numerical simulation.

关键词: heterogeneous network, social reinforcement, multi-stage, optimal resource allocation

Abstract: In this paper, we propose a heterogeneous multi-stage model to study the effect of social reinforcement on information propagation. Both heterogeneity of network components and the heterogeneity of individual reinforcement thresholds are considered. An information outbreak condition is derived, according to which the outbreak scale and individual density of each state under specific propagation parameters can be deduced. Monte Carlo experiments are conducted in Facebook networks to demonstrate the outbreak condition, and we find that social reinforcement effects generally inhibit the propagation of information though it contributes to the emergence of certain hot spots simultaneously. Additionally, by applying Pontryagin's Maximum Principle, we derive the optimal control strategy in the case of limited control resources to maximize the information propagation. Then the forward-backward sweep method is utilized to verify its performance with numerical simulation.

Key words: heterogeneous network, social reinforcement, multi-stage, optimal resource allocation

中图分类号: (Dynamics of social systems)

87.23.Ge

89.70.-a (Information and communication theory) 87.55.de (Optimization) 89.75.-k (Complex systems)

梅人杰, 丁李, 安栩明, 胡萍. Modeling for heterogeneous multi-stage information propagation networks and maximizing information[J]. 中国物理B, 2019, 28(2): 28701-028701.

Ren-Jie Mei(梅人杰), Li Ding(丁李), Xu-Ming An(安栩明), Ping Hu(胡萍). Modeling for heterogeneous multi-stage information propagation networks and maximizing information[J]. Chin. Phys. B, 2019, 28(2): 28701-028701.

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Modeling for heterogeneous multi-stage information propagation networks and maximizing information

Modeling for heterogeneous multi-stage information propagation networks and maximizing information

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