Research On Complex Network Visualization Technology Based On Compression And Cluster Analysis

Complex networks are networks with parts or all of characters including self-organization,self-similar,attractors,small worlds,scale-free parts or all of them.Real networks,such as social networks,transport networks,and so,have complex network characteristics.The visualization of complex networks is a broad concept,it is basically defined as the visualization technology based on a reasonable layout.In the broad sense,it can also include compression-based network fidelity analysis and clustering-based structured analysis.The research work of this paper is the study of generalized visualization technology.The main purpose of the paper is for the decision-makers of the network analyze to better grasp the main members of the network,structural level relationship as a whole.The purpose of the study on complex network compression is to show the main nodes and main relations of the network more clearly and to reduce the complexity of large-scale network analysis.The subsequent community mining algorithm and visual layout algorithm are based on the results of the compression algorithm.The compression method is based on graph theory analysis.According to the principle of network dynamics,the node is the main cause of the local network,the edge is the main cause of the overall network.The network is compressed by node and edge respectively in this paper.The importance of nodes is based on the degree of nodes and aggregation factors,because the degree of nodes reflects the aggregation ability of nodes themselves,and the aggregation coefficients of nodes reflect the influence of nodes on the local aggregation ability of neighbor nodes.The importance of the edge is based on betweenness,because the indicator reflects the ability to connect the different parts of the network.The proposed compression algorithm is validated by simulation data and real data,the experimental results show that the compressed network can maintain 60-80% of the original information when the compression ratio is as high as 30-50%,and still show the topology of the original network well.In practical application,according to the original network size,intensity and user need,the appropriate compression ratio can be selected.Based on the community characteristics of complex networks,this paper proposes a community mining clustering algorithm based on core nodes.The algorithm uses the higher importance nodes analyzed by the compression algorithm as the initial seed nodes,which ensures the local aggregation of the seed nodes,which is beneficial to improve the clustering efficiency and the effect.In this paper,the corresponding solutions are given to solve the possible overlapping problems of community mining.First,the core nodes are screened according to the distance between nodes.Second,process the results of overlapping community division.The optimization of the clustering process is reflected in the calculation of the fitness function,which takes into account the factors of community aggregation and community density.The main design of clustering analysis is given,including core node selection,fitness function calculation,overlapping node processing and so on.Experimental results show that the proposed algorithm improves the quality of clustering compared with traditional algorithms.In order to obtain a clear and intuitive complex network topology,this paper presents a visualization algorithm based on community structure.Based on the algorithm of force guidance layout and stratification,the algorithm uses the community structure obtained by clustering,to spread out from top to bottom.KK algorithm based on community compactness is used for the macroscopic layout between communities,and the FR algorithm based on circular display is used for the microscopic layout of nodes in the community.The experimental results show that the improved visualization layout is beautiful and the time efficiency is also good.In addition,the algorithm can also be used to assist in evaluating community clustering results.Due to the limitation of the calculation amount,the experimental results of the paper are based on the limited network scale,but the characteristics of the complex network are not limited to the network scale.The research work of the paper still makes sense for the large-scale network.