Research On The Method Of Identifying Influential Nodes In Complex Network

With the rapid development of information technology,complex network has become an important subject of domestic and foreign scholars.In the process of complex network research,the researchers collected large number of real data,and summed up the characteristics of complex networks in different areas,found that the issue of the complex network influence nodes in the study has a very important significance.At present,the researchers propose a variety of methods for the problem applicable to a variety of areas,these methods for the impact of node recognition research and development play a catalytic role.In this paper,the subject of analysis and research,the main contents are as follows.First of all,this paper summarizes the topological characteristics of complex networks,and deeply studies the influencing node recognition algorithms in complex networks.The statistical properties of complex networks such as degree distribution,average path length and clustering coefficients play an important role in the detection of node influence,and summarizes the classical node influence algorithm.Secondly,the algorithm of influencing node recognition based on edge centrality weighting is proposed.By introducing the center of the edge,the algorithm can estimate the degree of intimacy between nodes.The degree of contribution between nodes is determined by the degree of intimacy between nodes,the weights are given to the edges in complex networks,and the measure of the influence of nodes in the network is calculated by combining the Page Rank algorithm to identify the influencing nodes.Then,an algorithm for determining the influence node based on the adjacency Jaccard distance is proposed.The algorithm uses the Jaccard distance of the neighbor sets between adjacent nodes to measure the potential influence between the nodes,and the complex network is weighted.Combined with the k-shell algorithm to divide the nodes in the network,and get the measure of the influence of the nodes in the network,so as to select the influential nodes.Finally,based on the two algorithms proposed in this paper,the experiment is carriedout on the data set of the real complex network and compared with the classical algorithm.The performance of the two algorithms proposed in this paper is verified and the validity of the algorithm is verified.