Modeling for heterogeneous multi-stage information propagation networks and maximizing information
Modeling for heterogeneous multi-stage information propagation networks and maximizing information作者机构:School of Electrical Engineering and AutomationWuhan UniversityWuhan 430072China
出 版 物:《Chinese Physics B》 (中国物理B(英文版))
年 卷 期:2019年第28卷第2期
页 面:454-463页
核心收录:
学科分类:050302[文学-传播学] 05[文学] 07[理学] 070104[理学-应用数学] 0503[文学-新闻传播学] 0701[理学-数学]
基 金:Project supported by the National Natural Science Foundation of China(Grant No.61873194)
主 题:heterogeneous network social reinforcement multi-stage optimal resource allocation
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.