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

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

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.

Keywords: heterogeneous network social reinforcement multi-stage optimal resource allocation

Received: 05 October 2018
Revised: 19 November 2018
Accepted manuscript online:

PACS:	87.23.Ge	(Dynamics of social systems)
	89.70.-a	(Information and communication theory)
	87.55.de	(Optimization)
	89.75.-k	(Complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61873194).

Corresponding Authors: Li Ding
E-mail: liding@whu.edu.cn

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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 2019 Chin. Phys. B 28 028701

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