An Evaluation Method Of The Influence Of Nodes In Social Networks Based On Topology Potential

Along with the rapid economic and social development of Internet technology,people's lifestyles and means of communication appeared revolutionary change.Especially,under the background of the rapid progress of mobile Internet technology and the popularity of intelligent terminals,human social forms showed the networked trends,communication between people are becoming more and more frequent and closer,and information dissemination faster and faster,gradually formed a relationship between man and interwoven social networks,showing a complex,dynamic,large-scale and since similar characteristics.For research and analysis of social networks has been widely applied to information,economic,military and other security areas,due to the interdisciplinary social networks,complex and dynamic characteristics,its in-depth analysis and master there are some difficulties and challenges,in particular node,respectively,to complete the identification and assessment of the importance of community structures from the micro and macro,with a very great practical significance and value.For the past on Social Network mostly carried out on a simple graph,node importance in the assessment did not consider the intrinsic properties between nodes and node relations and multilateral relations cannot express the problem,the use of ultra-build a social network graph theory hypergraph model,the inherent properties of the nodes and the links between them were introduced into the model.Then introduced the cognitive physics data nodes in topological field theory to quantify the potential importance of nodes,respectively,the use of entropy method,Freud node quality algorithms and calculate the shortest path to the ideal combination of impact factor is taken value analysis,presented at a social network hypergraph model improved removal policy evaluation algorithm based on the importance of the node.Finally,the evaluation methods based on the node importance was used for the community recognition,using asynchronous thought to build up the random walk model based on the probability of node important degree gain and reachable strategy.Based on the introduction of random walk node similarity measure function and iterative update function,the community recognition algorithm was proposed based on social hierarchy node important function gain as the application and evaluation of performance of the algorithm verification nodes in order of importance,and through experiments to verify the correctness and feasibility of the algorithm.