摘要
网络空间要素及要素间关系构成的复杂网络进行可视化对感知网络结构和发现网络空间规律具有重要意义。针对网络空间复杂网络可视化时出现的大量节点和连边相互压盖造成的视觉混乱问题和基于层次聚类思想的社团划分算法构建的多尺度网络只能得到有限的尺度问题,提出一种基于社团结构的网络空间复杂网络多尺度构建方法。该方法基于改进的LFM社团发现算法,结合节点综合重要性进行社团核心节点的选取和依据社团间连边数量设定阈值建立重要节点不同层次连边,通过调整参数实现网络空间复杂网络多尺度构建。实验结果表明,该方法在构建多尺度网络过程中能够保留网络空间中的重要节点,相较于基于层次聚类思想的Louvain社团发现算法能够发现更多尺度的网络结构,且具有较好的连续性。
The visualization of complex networks composed of cyberspace features and the relationships between them is of great significance for perceiving network structure and discovering cyberspace laws.To address the visual confusion caused by a large number of nodes and edges overlapping each other in the visualization of complex networks in cyberspace and the problem that the multi-scale network constructed by the community division algorithm based on hierarchical clustering idea can only obtain a limited hierarchy,this paper proposes a new multi-scale construction method of complex network in cyberspace based on community structure.Utilizing the improved LFM community discovery algorithm,this method selects the core nodes of the community based on the comprehensive importance of nodes,sets the threshold according to the number of connections between communities,establishes the connections of important nodes at different levels,and realizes the multi-scale construction of complex networks in cyberspace by adjusting parameters.Experimental results show that the proposed method can retain important nodes in cyberspace in multi-scale networks constructing,and compared with Louvain algorithm,it can find more scale network structures and has good continuity,providing a new method for drawing multi-scale cyberspace maps.
作者
胡涛
李响
王丽娜
芦鹏飞
HU Tao;LI Xiang;WANG Lina;LU Pengfei(Information Engineering University,Zhengzhou 450001,China;Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《信息工程大学学报》
2024年第1期52-57,共6页
Journal of Information Engineering University
基金
国家自然科学基金青年科学基金资助项目(42201490)。
