基于谱聚类的二分网络社团检测算法  

Community Detection Algorithm for Bipartite Networks Based on Spectral Clustering

作　　者：刘晨晨 许英[1] LIU Chenchen;XU Ying(School of Statistics and Data Science,Xinjiang University of Finance and Economics,Urumqi 830012,China)

机构地区：[1]新疆财经大学统计与数据科学学院,新疆乌鲁木齐830012

出　　处：《吉首大学学报（自然科学版）》2023年第6期9-13,19,共6页Journal of Jishou University(Natural Sciences Edition)

基　　金：国家自然科学基金资助项目(72164034)。

摘　　要：针对二分网络社团检测算法存在精度不高和丢失原始网络信息等问题,设计了一种新的融合奇异值分解的谱聚类(SVD-MS)算法.该方法是将Barber的二分网络模块度最大化问题映射到奇异值向量分解上,并结合启发式算法快速求解向量划分问题.在3个真实世界的网络中对比SVD-MS算法与7种算法的模块度,结果表明,在保留原始网络信息的情况下,SVD-MS算法能更有效地划分二分网络的社团结构.In order to solve the problem of low precision and original network information lose in community detection of bipartite network,a new spectral clustering algorithm named SVD-MS is proposed.This method maps Barber's problem of maximizing the module size of bipartite networks to the problem of singular value vector decomposition,and combines heuristic algorithms to quickly solve vector partitioning problems.Experimental results show that,the SVD-MS algorithm can effectively partition the community structure of bipartite networks and preserve the original network information.

关 键 词：二分网络 社团检测 模块度 奇异值分解 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]