计算机工程与应用2024，Vol.60Issue(11)：258-267,10.DOI:10.3778/j.issn.1002-8331.2305-0108
自编码模块化增强非负矩阵分解社区检测算法
Community Detection Algorithm with Autoencoding-Like Modular Enhanced Non-Negative Matrix Factorization
摘要
Abstract
Community detection has been one of the key research directions in network analysis.Most of the current net-work community detection algorithms mainly use the structural information of the network to adopt a greedy algorithm to maximize a certain indicator,which cannot fully consider the node feature information,edge weight,and network commu-nity relationship asymmetry.To address this situation,this paper proposes an autoencoder-like modularity nonnegative matrix factorization(AMNMF)community detection algorithm.The algorithm expands the depth of non-negative matrix factorization by using an encoder-like structure,and introduces modularity and graph regularizer into the objective function optimization process of non-negative matrix factorization to fully mine the node and community structure information in the network.The problem of community relationship imbalance is solved by adding orthogonal constraints to the middle layer of the encoder.Experiments on multiple real networks show that:AMNMF is an effective NMF extension algorithm that uses node feature information and network structure information.Compared with the best results of baseline algorithms,it achieves an improvement of about 15%to 122%,and can accurately and effectively complete the community detection task.关键词
非负矩阵分解/社区检测/自编码/模块化Key words
nonnegative matrix decomposition/community testing/autoencoding/modular分类
信息技术与安全科学引用本文复制引用
朱玉龙,刘建忠,张寅宝,张欣佳,宋勇成,刘思聪,王雅博..自编码模块化增强非负矩阵分解社区检测算法[J].计算机工程与应用,2024,60(11):258-267,10.基金项目
国家社科重大项目基金(20&ZD138). （20&ZD138）
