Research On Maximization Of Node Influence And Propagation Model In Social Network

With the continuous enhancement and improvement of Web2.0 technology,people are more and more keen to use online social network platform for information exchange and product release.How to maximize the influence of social network information dissemination has become one of the current research hotspots.Based on the current research,the main work of this paper is as follows:(1)For the SI(Susceptible-Infected)information propagation model,the influence of intimate relationship between nodes on information propagation is not considered.This paper proposes an ESI(Extended Susceptible-Infected)information propagation model.In order to avoid the improper selection of the distance between the initial nodes of the excavation and the local optimal influence,a new heuristic algorithm,Core Reconstitutions Algorithm(CRA),is proposed.The algorithm introduces the concept of k-order core set and coincidence rate P.The coincidence rate P is used to reasonably control the influence range of the initial node,and the nodes with the best influence are found in turn.Experiments based on Sina Weibo show that the ESI propagation model is superior to the SI propagation model,and the CRA algorithm has better global impact than the existing heuristic algorithms.(2)In the actual social network,the different products of the same subject are full of competition.Based on the SI(Susceptible-Infected)information dissemination model,this paper considers the influence of competition between products on the nodes and improves it into an RSI.(Rival Susceptible-Infected)Competitive Information Dissemination Model.In a real social network,one of the products in the process of node propagation,other products have a competitive obstacle to the node.Considering the influence of the product's competitive relationship on the nodes and the distance between the initial nodes,a new heuristic algorithm,the Influence Reconstitutions Algorithm(IRA),is proposed.Experiments based on real data sets show that the IRA algorithm has better competitive effects than the existing heuristic algorithms in the competitive social network.(3)Based on the research results of competition-based social network maximization,based on the proposed competitive information dissemination model RSI and influence maximization algorithm,a competition-based social network impact maximization node mining prototype system is designed and implemented.