Modeling And Popularity Prediction Of Coevolution Spreading In Complex Networks

The development of complex networks provides a new perspective for the study of complex systems in the real world.Modeling and prediction of propagation on complex networks have always been a hot research topic.However,Spreading processes in real complex systems is rarely independent,such as information and epidemic,epidemic and resource,information and information,etc.They evolve together with interactions of each other.The forms of coevolution spreading can be divided into competition,and asymmetric dissemination.In order to describe the phenomenon of coevolution spreading in the real world better,our work focuses on the modeling and popularity prediction of coevolution spreading in complex networks,which mainly includes two parts as follows:Fisrt,we study the coevolution spreading of resource and epidemic.As one typical type of coevolution,the dynamic of coevolution spreading of resource and epidemic has been widely concerned by scholars.Some previous works have studied that the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks.However,the social relationships of individuals are actually temporal,which obviously affect such coupled dynamics.For that,we propose a coupled resource-epidemic dynamic model on the temporal multiplex networks.In the model,the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states.By using the microscopic Markov chain approach and Monte Carlo simulations,we determine a probabilistic framework of the intra-layer and inter-layer dynamic processes of the coupled model,and obtain the outbreak threshold of epidemic spreading.Meanwhile,the experiments show the trivially asymmetric interactions between resource diffusion and epidemic spreading.And,they also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion,which effectively suppresses epidemic spreading.However,these two characters of individuals in the epidemic layer can cause more resource depletion,which greatly promotes epidemic spreading.Furthermore,we also find that the contact capacities of individuals finitely impact the coupled dynamics of the resource diffusion and epidemic spreading.Second,we also propose two other co-evolutionary models oriented to popularity prediction: m SSIR(Multi-dimensional Stochastic SIR)and m Hawkes N(Multi-dimensional HawkesN).Based on the classic stochastic SIR model and Hawkes point process model,respectively,they are extended to the scenarios of coupled propagation dynamic both on the single layaer and multi-layer networks.What's more,the models can embody all of three forms of coevolution spreading.The simulation data and real Weibo data verify that the models achieve superiority in popularity prediction than the previous independent Hawkes N.Finally,this paper also establishes the link between parameters in m SIR and mHawkesN,which provides more solutions for the study of co-evolution and propagation in complex network.