Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3120-3126.DOI: 10.11772/j.issn.1001-9081.2021010043

• Artificial intelligence • Previous Articles     Next Articles

Community detection method based on tensor modeling and evolutionary K-means clustering

Jicheng CHEN(), Hongchang CHEN   

  1. Institute of Information Technology,Information Engineering University,Zhengzhou Henan 450002,China
  • Received:2021-01-15 Revised:2021-03-25 Accepted:2021-04-01 Online:2021-04-15 Published:2021-11-10
  • Contact: Jicheng CHEN
  • About author:CHEN Jicheng,born in 1982,Ph. D. candidate,His research interests include complex network,community detection and discovery.
    CHEN Hongchang,born in 1964,Ph. D.,professor. His research interests.
  • Supported by:
    the Innovative Research Group Project of National Natural Science Foundation of China(61521003)


陈吉成(), 陈鸿昶   

  1. 信息工程大学 信息技术研究所,郑州 450002
  • 通讯作者: 陈吉成
  • 作者简介:陈吉成(1982—),男,江苏淮安人,博士研究生,主要研究方向:复杂网络、社区检测与发现
  • 基金资助:


Most traditional community detection methods are limited to single relational network, and their applicability and accuracy are relatively poor. In order to solve the problems, a community detection method for multiple relationship networks was proposed. Firstly, for modeling the multiple relational network, the third-order adjacency tensor was used, in which each slice of the tensor represented an adjacency matrix corresponding to a type of relationship between participants. From the perspective of data representation, by interpreting the multiple relational network as a third-order tensor is helpful to use the factorization method as a learning method. Then, RESCAL decomposition was used as a relational learning tool to reveal the unique implicit representation of participants. Finally, the evolutionary K-means clustering algorithm was applied to the results obtained in the previous step to determine the community structure in multiple dimensions. The experiments were conducted on a synthetic dataset and two public datasets. The experimental results show that, compared with Contextual Information-based Community Detection (CICD) method, Memetic method and Local Spectral Clustering (LSC) method, the proposed method has the purity at least 5 percentage points higher, the Overlapping Normalized Mutual Information (ONMI) at least 2 percentage points higher, and the F score at least 3 percentage points higher. And it is proved that the proposed method has fast convergence speed.

Key words: community detection, multiple relational network, RESCAL decomposition, evolutionary K-means clustering, third-order adjacency tensor



关键词: 社区检测, 多关系网络, RESCAL分解, 进化K均值聚类, 三阶邻接张量

CLC Number: