Research On Emotion-driven Topological Evolution And Phase Transition In Social Networks

With the development of online social media,people frequently use social media to update their daily and convey information.And these behaviors are exhaustively recorded by digital media,and this vast amount of data on human communication behaviors provides the basis for studying human communication behaviors.Network science gives us a new perspective to understand the complexity of human interactions more deeply and thus better grasp the diversity of communication processes.Phase transition is a physical process in which a substance suddenly changes from one phase to another under continuous changes in external parameters.However,this phase transition phenomenon is not unique to physical systems,but also exists in other complex systems,among which the outburst phenomenon in complex networks is one of the phase transition phenomena in networks.Therefore,this work examines the process of information spreading in social networks from the perspective of phase transition.The study conducted in this work covers the following two main aspects.(1)In this thesis,the influence of users’ attention mechanisms on the topology of social networks is studied from the perspective of phase transition.Based on the characteristics of real social networks,a hybrid network model consisting of small-world networks and scale-free networks is constructed.In addition,the attention mechanism of network users in information spreading is studied from four aspects: social distance,individual influence,individual content richness and individual activity,and a dynamic evolution model of “connecting with spreading”is designed.The proposed model is simulated numerically in three networks.The simulation results show that topological structure and node influences in networks have undergone phase transition,which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period.The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule.Furthermore,the simulation results are compared with the real data,and the result shows that the infection density curve of the hybrid networks is closer to the real data than the infection density curve of the small-world networks and scalefree networks.(2)In this thesis,the evolutionary laws of group emotion and network structure in the process of information spreading are studied from the perspective of phase transition.Based on the communication characteristics of real events that emerge different information fragments at different communication stages,an emotion communication model based on information coupling is proposed.And the types of real events in social networks are divided into three categories: positive events,neutral events,and negative events,respectively.The effect of information coupling on the evolution of individual and group emotions is explored,and a topological evolution model based on attractiveness and emotional similarity among individuals is proposed.The simulation results show that with the increasing weight of event sensitivity,the earlier the phase transition point of group emotion is,the higher the phase transition degree is.In addition,the traditional SI model and the topological evolutionary model proposed in this thesis are experimented in small-world networks and scale-free networks,respectively.The simulation results are compared with the real data of the three types of events,and it is found that the infection density curves after introducing the event sensitivity parameters in the topological evolution model based on attractiveness and emotional similarity are closer to the real data of the three types of events than the infection density curves of the traditional SI model.