Design And Implementation Of Visualization Analysis System For Complex Networks

With the development of intelligent technology,the Internet of Things,and other technologies,data has entered an explosive growth stage.In this context,complex networks have been widely used to solve various practical problems.Complex network visualization technology helps users to better understand the network structure,and to explore important features and rules in the network by converting the complex network data into easily understandable images.Currently used visualization technologies cannot accurately represent network structures and relationships,cannot effectively display network properties and features,and have low layout algorithm efficiency,which cannot meet the demands of large-scale complex networks.This article focuses on researching complex network clustering layout algorithms and complex network visualization and analysis methods,and designs and implements a prototype system for complex network visualization and analysis.This system can effectively improve the comprehensibility and analysis efficiency of data,and discover the characteristics and rules present in the data.The main work of this article is as follows:(1)A clustering layout algorithm called FR-SC(Fruchterman-Reingld Spatial Cluster)based on the force-directed model is designed and implemented.This is a force-directed clustering layout algorithm that uses multi-level thinking.Firstly,the network is partitioned into subnets using the Louvain algorithm to form a set of communities.Then,the complex network clustering layout is performed,community size and position are calculated,community force is introduced to layout nodes,highlights the network community structure,and improves algorithm efficiency by combining the quadtree spatial decomposition.Finally,the proposed FR-SC layout algorithm is demonstrated to be superior to the commonly used FR(Fruchterman-Reingold)algorithm in terms of network features and algorithm execution time.For large-scale complex networks with thousands of nodes and edges,the algorithm can complete the calculation within 15 seconds,and the network structure features are clear.It is suitable for large-scale Internet,urban transportation network,social network,power network and other large complex networks.(2)Research is carried out on complex network visualization and analysis methods.Two visualization analysis methods,including complex network coarsening and complex network correlation analysis,are designed and implemented.The network coarsening visualization method is based on the Welzl algorithm to calculate the minimum bounding circle of each community node,simplifying the network structure.The network correlation analysis visualization method realizes two views: 2D and 3D.Among them,the 2D view requires steps including node layout,node position mapping,and visualization presentation,and can achieve correlation analysis of any number of communities.The 3D view of the correlation analysis not only includes steps such as node layout,position mapping,and visualization presentation but also provides camera angle and position control,helping users to observe the network from any angle and position to avoid inconvenience caused by blocked viewing.(3)Combining the proposed force-directed model-based FR-SC layout algorithm,complex network coarsening visualization method,and complex network correlation analysis visualization method,a prototype system for complex network visualization and analysis is designed and developed.The system optimizes network layout using a clustering layout algorithm,allows users to efficiently obtain information and rules about network data through visual analysis methods,and accurately understand important sections and data characteristics.In addition,the system provides multiple network analysis modules,including overall situation,community situation,shortest distance query,and view filtering,to further help users analyze complex network data.