计算机应用研究2025，Vol.42Issue(1)：42-47,6.DOI:10.19734/j.issn.1001-3695.2024.06.0190
融合节点属性的局部多重社区发现算法
Multiple local community detection with integrated node attributes
摘要
Abstract
Multiple local community detection is a key technology in social network analysis,aiming to reveal the multiple af-filiations and complex connections of users within networks.Addressing the issue that most existing multiple local community detection algorithms are based on network topology and neglect node attribute information,this paper introduced an algorithm named multiple local community detection with integrated node attributes(MLCDINA).This algorithm combined the structure and attribute information of the attributed network to determine the edge weights between node pairs and evaluated the impor-tance of the integration of structure and attributes(IISA)through random walks.In addition,the algorithm introduced a local clustering coefficient that considered edge weights and an intimacy random walk(IRW)to enhance the evaluation of subgraph density and IISA.Experimental results indicate that MLCDINA significantly improves the Jaccard F1-score over existing algo-rithms on real attributed networks,verifying its effectiveness in multiple local community detection tasks.关键词
局部社区发现/属性网络/随机游走Key words
local community detection/attributed network/random walk分类
数学引用本文复制引用
陈李舟,冯俊又,徐煊翔,刘先博,杜彦辉..融合节点属性的局部多重社区发现算法[J].计算机应用研究,2025,42(1):42-47,6.基金项目
中国人民公安大学网络空间安全执法技术双一流创新研究专项资助项目 （）
