摘要
谱社区检测算法多基于结构对网络进行划分,往往受限于划分数量且难以控制重叠程度。设计了面向属性网络的谱社区检测算法,可将属性网络划分为任意数量的可重叠社区并有效发现离群点。具体地,首先,从结构和属性两方面综合考虑,基于加权模块度设计了最大化到节点向量化的分区映射方法;其次,给出簇中心向量的初始选择策略,并将其融合在面向属性网络的重叠度和离群度制约中,实现重叠社区的发现;再次,设计节点分配策略,计算节点与簇中心向量的内积,将节点分配给具有最高内积的社区;最后,结合节点隶属情况,高效地在属性网络中检测出结构紧密、可重叠和具有离群点的社区。此外,将本文算法应用于现实世界的多个网络,验证了本文算法的有效性和效率。
Spectral community detection algorithms generally divide the network via structure,which is often limited by the number of divisions and it is difficult to control the degree of overlapping.This paper designs an overlapping multiway spectral community detection algorithm for attribute network,which can divide the attribute network into any number of overlapping communities and effectively discover outliers.Firstly,the partition mapping method from maximization to node vectorization is designed based on the weighted modularity.Secondly,the initial selection strategy of cluster center vectors is given and merged in the attributed network.Thirdly,the node allocation strategy is designed to calculate the inner product of the node and clustering center vector and to assign the node to the community with the highest inner product.Finally,the tightly structured overlapping communities that have out-liers are effectively detected.In addition,applying the algorithm to multiple networks in the real world verifies the effectiveness and efficiency of the proposed algorithm.
作者
李青青
马慧芳
吴玉泽
刘海姣
LI Qing-qing;MA Hui-fang;WU Yu-ze;LIU Hai-jiao(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004;School of Management,Gansu Agricultural University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期984-992,共9页
Computer Engineering & Science
基金
国家自然科学基金(61762078,61363058,61966004)
广西多源信息挖掘与安全重点实验室开放基金(MIMS18-08)
西北师范大学2019年度青年教师科研能力提升计划重大项目(NWNU-LKQN2019-2)。
关键词
属性网络
多向谱算法
可重叠社区
离群点
attributed network
multiway spectral algorithm
overlapping community
outlier
