计算机与数字工程2024，Vol.52Issue(2)：477-481,577,6.DOI:10.3969/j.issn.1672-9722.2024.02.033
基于重叠社区发现的网络数据可视化优化方法研究与实现
Research and Implementation of Network Data Visualization Optimization Method Based on Overlapping Community Discovery
摘要
Abstract
With the surge in data volume,the relationship between data has become intricate and complicated,which brings challenges to network visualization.Through community discovery,highlighting the local clustering characteristics in the network can improve the visualization effect,and the discovery of overlapping communities can be closer to the actual network structure.The Louvain algorithm with simple,efficient and fast execution speed is currently one of the most commonly used community discovery algorithms,but the discovery of overlapping communities is its shortcoming.To this end,the paper is based on the Louvain algo-rithm,combined with the fuzzy C-means clustering algorithm based on spectral mapping to improve the community discovery algo-rithm.The improved algorithm uses spectral mapping to map data nodes to Euclidean space,the degree of membership is used to calculate the degree to which a data node belongs to a certain cluster,which allows the same data to belong to multiple different classes,thereby realizing the discovery of overlapping community structures.Finally,based on the proposed algorithm,the FR mod-el in the mainstream layout algorithm is used to visualize the network data.Using the modularity value as an evaluation indicator,the experimental results show that the method proposed in the paper can find overlapping communities and can clearly show the com-munity structure in the network,compared with the traditional overlapping community discovery algorithms COPRA and CPM on the classic data set,modularity values is improved.关键词
社区发现/Louvain算法/模糊聚类方法/布局算法/图可视化Key words
community discovery/Louvain algorithm/fuzzy clustering method/layout algorithm/graph visualization分类
信息技术与安全科学引用本文复制引用
解蓝莹,周莲英,谢超..基于重叠社区发现的网络数据可视化优化方法研究与实现[J].计算机与数字工程,2024,52(2):477-481,577,6.基金项目
慢病本体知识库开发项目(编号:8421170004) （编号:8421170004）
市智慧妇幼信息平台本体知识库系统项目(编号:20180477)资助. （编号:20180477）
