Abstract
As the theory of complex networks is further studied, the scale of nodes in the network is increasing, which makes it difficult to find useful patterns from only the analysis of nodes. Therefore, this paper proposes a complex network node layout method based on community compression, which can effectively display the mesoscale structure characteristics of the network, making it more convenient for users to analyze the status and function of a single node or a class of nodes in the whole complex network. To begin with, the whole network is divided into communities with different granularity by the Louvain algorithm. Secondly, the method of nodes importance analysis based on topological potential theory is extended from the network to the community structure, and the internal nodes of the community are classified into three types, namely important nodes, relatively important nodes, and fringe nodes. Furthermore, a compression algorithm for the community structure is designed to realize the compression of the network by retaining important nodes and merging fringe nodes. Finally, the compression network is laid out by the traditional force-directed layout method. Experimental results show that, compared with the compression layout methods of a complex network based on degree or PageRank, the method in this paper can retain the integrated community composition and its internal structure, which is convenient for users to effectively analyze the topology structure of a complex network.
Highlights
The purpose of network visualization is to assist users to perceive the network structure and understand and explore patterns hidden in the network data [1,2]
Li et al [22] divided network nodes based on the K-core concept in complex networks, and used the force-directed layout algorithm to realize the visualization of a compressed large-scale complex network
In order to analyze the mesoscale structure characteristics of the network effectively, this paper combined the force-directed layout algorithm with the community structure characteristics of the network, and proposed a method of node layout of a complex network based on community compression
Summary
The purpose of network visualization is to assist users to perceive the network structure and understand and explore patterns hidden in the network data [1,2]. On the one hand, when a complex network with large numbers of nodes and high-density edges is visualized on a limited-size screen, users fall into a chaotic and overlapping node-connection diagram, where it is difficult to identify and perceive the overall structure of the network, and the users cannot find elements of interest. The existing work of complex networks has been limited to the local scale structure, obtained through statistical distribution, or the macroscopic scale of the overall parameters of the network These two levels can be understood through the intermediate level, which is called the mesoscale structure [3]. In order to display information effectively and assist users in perceiving the mesoscale structure of the network, a large-scale network must be reduced to bring down the user’s perception complexity and the computational complexity of the layout process. The method proposed in this paper can be used to visually observe and analyze the mesoscale structural features of the network, understand the rules and patterns within the network from the mesoscale, and provide support for the research of the synchronization process of the community network
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