融入模体信息的多层网络社区发现算法  

Multi⁃layer network community discovery algorithm incorporating motif information

作　　者：赵兴旺[1,2] 张超[1] 梁吉业[1,2] Zhao Xingwang;Zhang Chao;Liang Jiye(School of Computer and Information Technology,Shanxi University,Taiyuan,030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing,Ministry of Education,Shanxi University,Taiyuan,030006,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]计算智能与中文信息处理教育部重点实验室(山西大学),太原030006

出　　处：《南京大学学报(自然科学版)》2024年第6期954-969,共16页Journal of Nanjing University(Natural Science)

基　　金：国家自然科学基金(62072293,U21A20473);山西省基础研究计划(202403021211086,202303021221054);山西省回国留学人员科研资助项目(2024-002)

摘　　要：多层网络社区发现算法旨在揭示复杂网络中蕴含的社区结构,近年来得到了广泛关注,然而现有算法在度量节点相似度的过程中往往只关注网络中的低阶结构信息,忽略了高阶结构信息,而且,在对不同层网络进行融合的过程中也没有考虑不同层之间的差异性.针对以上问题,提出一种融入模体信息的多层网络社区发现算法.首先,各层分别计算融入模体的高阶邻接矩阵,通过与低阶邻接矩阵融合得到重构矩阵,进而基于邻居重要性对重构矩阵进行提升,得到节点相似度矩阵;其次,基于重构矩阵计算各层网络的重要性,再加权融合得到统一的相似度矩阵;最后,基于统一的相似度矩阵得到节点的影响力,通过节点嵌入表示方法,对节点的向量表示进行迭代更新,得到节点的最终嵌入表示.与已有的传统多层网络社区发现算法进行了对比实验,结果表明,提出的算法的多层模块度和标准化互信息等评价指标均优于已有算法.The multi‐layer network community discovery algorithm aims to reveal the community structure of complex networks and has received widespread attention in recent years.However,existing algorithms only focus on the low‐order structural information between nodes when measuring node similarity,ignoring the utilization of high‐order structural information.Moreover,when fusing information from different layers of the network,there is a lack of consideration for the differences between different layers.To address these issues,the paper proposes a multi‐layer network community discovery algorithm incorporating motif information.Specifically,firstly,each layer calculates a high‐order adjacency matrix based on the motif information,fuses it with a adjacency matrix to obtain a reconstruction matrix,and then enhances the reconstruction matrix based on the importance of node neighbors to obtain a similarity matrix between nodes.Secondly,based on the reconstruction matrix,the importance of each layer of the network is calculated,and weighted fusion is used to obtain a unified similarity matrix.Finally,based on the obtained similarity matrix,the node influence is calculated,and the vector representation of the nodes is iteratively updated through the node embedding representation method to obtain the final embedding representation.Comparative experiments were conducted with existing multi‐layer network community discovery algorithms on artificial multi‐layer networks and real multi‐layer network data.The results indicate that the proposed algorithm outperforms existing algorithms in terms of multi‐layer modularity and normalized mutual information.

关 键 词：多层网络 社区发现 高阶信息 节点相似度 嵌入表示 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]