Research On The Layout And Compression Visualization Algorithm For Complex Networks

With the advent of the era of big data,the scale of data is getting larger and larger,and the huge amount of data is no longer suitable for the presentation of traditional data.Visualization technology is the most important means to help people understand and analyze complex networks.However,with the rapid development of information,complex networks are experiencing massive growth.It is very difficult to visualize complex networks in strict accordance with traditional layout algorithms.On the one hand,because traditional layout algorithm pays too much attention to aesthetic standards,after the data scale grows,no matter how to optimize it,it cannot avoid the problems of point overlap,edge crossover,etc.Besides,the layout ignores the feature of community structure,which is not conducive to people's understanding of the function and structure of complex network.On the other hand,there are too many nodes on the layout diagram,which can seriously affect people's observation.However,as can be seen from the scale-free characteristic,the importance of most nodes is low,while the layout of these nodes occupies most resources.Therefore,it is necessary to compress the complex network to improve the layout quality and shorten the layout time.In view of the above problems,the paper proposes two different algorithms from the two fields of layout and compression.The main contents include:1.Force-Directed based in community radius and radial tree hybrid layout algorithm is proposed.Force-Directed layout has the defects of display complex network community structure.Although the cluster layout algorithm can display the community structure,the nodes in the community are crowded,which is not conducive to observing the structural features and the connected relationship of nodes in the communi-ty.therefore,ForceDirected embedded in community radius and radial tree hybrid layout algorithm is proposed.Firstly,The algorithm uses the K-means algorithm to divide the network nodes into communities.Then,the community radius is determined by the number of nodes in each community,and the com-munity radius is embedded into the repulsion and gravity to achieve the effect of cluster layout.Finally,the radial tree layout is used for each community to hierarchically visualize nodes within the community.2.Node importance in edge triangle algorithm is proposedIn the face of large and complex network data scales,complex network analysis becomes increasingly difficult.In order to efficiently mine and analyze complex networks,a complex network filtering compression algorithm based on trianglesubgraph is proposed.Firstly,a node importance ranking algorithm is proposed to select high and low importance nodes to reduce the computational scale and shorten the compression time by filtering high and low importance node.Then,starting from the edge,the nodes at both ends of the edge and the set of common nodes form a set of triangle-subgraph.Finally,the triangle-subgraph set is parsed to complete the compression.In the visual layout algorithm,crowed area ratio,point distribution deviation,node deviation and other indicators are used to show that the algorithm can reduce the congestion and the node layout deviation.The visual results prove that the layout structure of the algorithm is obvious,and the nodes are clearly structure and easy to understand.In the visual compression algorithm,the SIR model is used to verify that the ordering result of the node importance sorting algorithm is reasonable and reliable.At the same time,from the compression time,compression rate,information retention rate and other indicators,the filter compression algorithm can shorten the compression time,improve the compression rate,and retain the structure and information of the original network.