Research On Analysis Method For Structure And Influence Of Social Networks

In the 21 st century,humans become highly dependent on social networks and deeply integrated into the information society.Social networks are constantly changing human behavior patterns and social patterns,and influencing the socioeconomic development and people's lifestyle.Structural characteristics are the essential characteristics of social networks,and the analysis of social networks structure is an important basis of the theoretical research and practical application of social networks,which has become a research hotspot and frontier topics in the current academic community.Research on social networks can make people further understand the structural characteristics of social networks,help people to recognize and explain social phenomena,adapt to the changes brought about by social networks to people's work,study,etc.Meanwhile,it can help further reveal the internal structural functions of social networks,understand individual behaviors and emotions of social networks,grasp the direction of social network development,effectively solve social problems,maintain social stability and coordinate social behavior,and provide technical support for sociology,management science and other disciplines.Therefore,the research on social networks structure and influence analysis has important theoretical research value and broad application prospects.The social networks structure analysis is studied from the macro,meso,and micro perspectives in this dissertation.On the one hand,it studies macroscopically the characteristics of social network topology and constructs a social network evolution model,which can provide theoretical and data support for the meso community identification and micro-nodal influence analysis.On the other hand,it studies how to design more feasible,efficient,and reliable methods to solve technical difficulties in social network community identification and influence maximization from the perspective of meso and micro perspectives,which can provide important theoretical support and technical support for conducting social network analysis and its application.In summary,the research content of this dissertation mainly includes the following four aspects:Firstly,as most of the existing social network evolution models often consider from the perspective of the overall network,but ignore the microscopic characteristics and information dissemination characteristics of the nodes and fail to describe the actual evolutionary process of the real network,a new method of constructing social network evolutionary model based on information communication characteristics is proposed in this dissertation.This method isbased on the information propagation characteristics of nodes,which comprehensively considers the similarity of domains between the nodes,the information dissemination attraction and the local dissemination characteristics.It also integrates the concept of the activity level of node information dissemination,which completes social networks model building by comprehensively and exquisitely simulating the information dissemination process between nodes.The results of experiments show that this method can portray and describe the topological characteristics and evolutionary characteristics of real networks more accurately.Secondly,considering that traditional non-overlapping community identification algorithms suffer from the problems such as the need for a preset number of communities,high time complexity,and low quality of community classification,this dissertation proposes a non-overlapping community identification algorithm based on circular spread label propagation.Based on the traditional label propagation algorithm,it adopts the pruning strategy to preprocess,introduces the node influence to measure,sort and group the nodes,and effectively solves the problems of strong randomness and poor recognition quality of the label propagation algorithm.The results of experiments demonstrate that the iterations of tags are reduced,and the stability of the propagation is enhanced and community classification is effectively improved by the new algorithm,while the near-linear linearity of tag propagation is retained.Thirdly,most of the existing research results in the field of overlapping community identification require manually setting the community overlap,which does not apply to the real networks,and the problems such as algorithm efficiency and community division quality need to be further improved,so this dissertation proposes a label propagation overlapping community identification algorithm based on membership degree.The algorithm first uses the similarity between nodes and the neighboring node structure to define the community membership grade of the node.At the same time,the topological potential of the node is introduced to measure the node influence and the tag is truncated by comparing with the node membership degree;Then,the circular spread strategy is used to improve the traditional label overlapping community identification algorithms and complete the overlapping community identification.The results of experiments show that this algorithm has high efficiency and stability,and can effectively improve the stability and quality of community identification results without setting parameters manually.Finally,most of the existing research work ignores the local structural characteristics of the network and the node influence range overlapping,resulting in unsatisfactory propagation effects and the inability to adapt to large-scale network problems.To solve the problem,analgorithm for maximizing social network influence based on community identification is proposed.The method of measuring node influence with topological potential is introduced in the algorithm.The candidate nodes are selected dynamically by localized community partitioning,and then,the problem of maximizing the influence in the social network is settled by community identification and the local edge deduplication.The experiments on real data sets demonstrate that compared with the major algorithms,the new one is more feasible and effective,and the propagation effects are better under the same conditions.