Model for multi-messages spreading over complex networks considering the relationship between messages

Abstract A novel messages spreading model is suggested in this paper. The model is a natural generalization of the SIS (susceptible-infective-susceptible) model, in which two relevant messages with same probability of acceptance may spread among nodes. One of the messages has a higher priority to be adopted than the other only in the sense that both messages act on the same node simultaneously. Node in the model is termed as supporter when it adopts either of messages. The transition probability allows that two kinds of supports may transform into each other with a certain rate, and it varies inversely with the associated levels which are discretely distributed in the symmetrical interval around original point. Results of numerical simulations show that individuals tend to believe the messages with a better consistency. If messages are conflicting with each other, the one with higher priority would be spread more and another would be ignored. Otherwise, the number of both supports remains at a uniformly higher level. Besides, in a network with lower connected degree, over a half of the individuals would keep neutral, and the message with lower priority becomes harder to diffuse than the prerogative one. This paper explores the propagation of multi-messages by considering their correlation degree, contributing to the understanding and predicting of the potential propagation trends.

[1]  Ljupco Kocarev,et al.  Topology independent SIS process: An engineering viewpoint , 2014, Commun. Nonlinear Sci. Numer. Simul..

[2]  Chenquan Gan,et al.  Propagation of computer virus both across the Internet and external computers: A complex-network approach , 2014, Commun. Nonlinear Sci. Numer. Simul..

[3]  Zhenguo Bai,et al.  Traveling waves of a diffusive SIR epidemic model with a class of nonlinear incidence rates and distributed delay , 2015 .

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[6]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Xun Li,et al.  Diffusion processes of fragmentary information on scale-free networks , 2016 .

[8]  Ljupco Kocarev,et al.  Model for rumor spreading over networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Zhong Chen,et al.  Impulsive synchronization of a general nonlinear coupled complex network , 2011 .

[10]  D. Kendall,et al.  Epidemics and Rumours , 1964, Nature.

[11]  Haiyan Wang,et al.  The free boundary problem describing information diffusion in online social networks , 2013 .

[12]  Haiyan Wang,et al.  On the Existence of Positive Solutions of Fourth-Order Ordinary Differential Equations , 1995 .

[13]  Hongyong Zhao,et al.  Bifurcation and control of a delayed diffusive logistic model in online social networks , 2014, Proceedings of the 33rd Chinese Control Conference.

[14]  Sujatha Yeruva,et al.  Selection of influential spreaders in complex networks using Pareto Shell decomposition , 2016 .

[15]  Xingyuan Wang,et al.  Model of epidemic control based on quarantine and message delivery , 2016, Physica A: Statistical Mechanics and its Applications.