计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 204-211.doi: 10.11896/jsjkx.210300060

• 数据库&大数据&数据科学 • 上一篇    下一篇


徐新黎, 肖云月, 龙海霞, 杨旭华, 毛剑飞   

  1. 浙江工业大学计算机科学与技术学院 杭州310023
  • 收稿日期:2021-03-05 修回日期:2021-06-08 发布日期:2021-11-26
  • 通讯作者: 毛剑飞(mjf@zjut.edu.cn)
  • 作者简介:xxl@zjut.edu.cn
  • 基金资助:

Attributed Network Embedding Based on Matrix Factorization and Community Detection

XU Xin-li, XIAO Yun-yue, LONG Hai-xia, YANG Xu-hua, MAO Jian-fei   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2021-03-05 Revised:2021-06-08 Published:2021-11-26
  • About author:XU Xin-li,born in 1977,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include intelligent computing,network embedding and medical image computing.
    MAO Jian-fei,born in 1976,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include network visual media and mobile media technology,computer,vision and embedded system.
  • Supported by:
    National Natural Science Foundation of China(61773348),Zhejiang Public Welfare Science and Technology Plan Project(LGG20F020017) and Natural Science Foundation of Zhejiang Province(LQ18F030015).

摘要: 属性网络不但包含节点之间复杂的拓扑结构,还包含拥有丰富属性信息的节点,其可以比传统网络更有效地建模现代信息系统,属性网络的社区划分对于分析复杂系统的层次结构、控制信息在网络中的传播和预测网络用户的群体行为等方面具有重要的研究价值。为了更好地利用拓扑结构信息和属性信息进行社区发现,提出了一种基于矩阵分解的属性网络嵌入和社区发现算法(CDEMF)。首先提出基于矩阵分解的属性网络嵌入方法,基于网络局部链接信息计算相邻节点的相似性,将其与属性接近度联合建模,通过矩阵分解的分布式算法得到每个节点对应的低维嵌入向量,即把网络节点映射为低维向量表示的数据点集合。接着提出基于曲率和模块度的社区划分方法,自动确定数据点集合中蕴含的社区数量,并通过对数据点集合聚类完成属性网络社区划分。在真实网络数据集上,将CDEMF方法与其他8种知名算法进行比较,实验结果表明CDEMF具有良好的性能。

关键词: 属性网络嵌入, 矩阵分解, 自动聚类, 社区发现, 曲率

Abstract: An attributed network contains not only the complex topological structure but also the nodes with rich attribute information.It can be used to more effectively model modern information systems than traditional networks.Community detection of the attributed network has important research value in hierarchical analysis of complex systems,control of information propagation in the network,and prediction of group behavior of network users.In order to make better use of topology information and attribute information for community discovery,an attributed network embedding based on matrix factorization and community detection(CDEMF) are proposed.First,an attributed network embedding method based on matrix factorization is proposed to model the attributed proximity and the similarity of adjacent nodes calculated in term of the local link information of the network,where the low-dimensional embedding vector corresponding to each node can be obtained by a distributed algorithm of matrix decomposition,that is,the network nodes can be mapped into a collection of data points represented by low-dimensional vectors.Then the community detection method based on curvature and modularity is developed to achieve attributed network community division by clustering the data point set,which can automatically determine the number of communities contained in the data point set.CDEMF is compared with the other 8 kinds of well-known approaches on public real network datasets.The experimental results demonstrate the effectiveness and superiority of CDEMF.

Key words: Attributed network embedding, Matrix factorization, Automatic clustering, Community detection, Curvature


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