Research On Network Modeling With Community Structure And Information Spreading

Many complex systems can be represented as complex networks,which have been used in very different application domains,such as social network,protein interaction network,metabolic network,etc.Such large real-world networks are sharing same properties,such as community structure,small world,scale-free characteristics etc.The study of topological properties and behaviors of complex networks has received an enormous amount of attention from the scientific community in recent years.Since then,researchers have actively pursued the development of network generation models to mimic the creation and evolution of complex networks emerging from a variety of real world interconnected systems.There is a substantial interest in using these synthetic networks to examine the impact of different dynamic processes on these networks like information diffusion and immunization strategies.This article focuses on how to generate network models,and studies the behaviors characteristics of information spreading and immunization strategy on these models.The main works are summarized as follows.(1)A network generation algorithm TCMSN is proposed to generate the scare-free complex networks with adjustable topological properties and community structure.By adjusting the input parameters(mixing parameters and preferential attachment probability)of the model,the topological properties of the generated network can be adjusted,such as modularity and clustering coefficient.TCMSN adopts a reasonable strategy for adding edges to networks to maintain the scale free characteristics without destroying network diversity.Experimental results show that the proposed TCMSN algorithm can generate network model closed to the community structure of the real networks.(2)We analyzed the characteristics of the information spreading on networks generated by TCMSN,compared with some real networks.Wecompare the spreading speed and infection scale between rich nodes and diverse nodes under different spreading models.Experimental results show that rich nodes have a faster spreading speed and larger infection scale than diverse ones.While diverse nodes are tend to have faster spreading speed and larger infection scale than non diverse nodes with same degree.In addition,we also analyzed the influence of community structure on information spreading and experimental results show that the spreading on network with larger modularity might have less spreading speed and scale.(3)An information immunization strategy CHBD(Community Hub and Bridge Detector)based on local information is proposed,which can effectively detect the potential bridge nodes and central nodes without knowing the global information and community structure of the network in advance.Though immunizing these nodes,we can effectively suppress the information spreading in the network.Simulation experiments on networks models and real networks show that the CHBD immunization strategy has a better effect on suppressing information spreading in networks with community structures.