A New Clustering Cover Algorithm Based on Graph Representation for Community Detection

CHEN Jie; LI Rui; ZHAO Shu; ZHANG Yan-ping

doi:10.3969/j.issn.0372-2112.2020.09.003

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A New Clustering Cover Algorithm Based on Graph Representation for Community Detection

更新时间：2025-12-08

- A New Clustering Cover Algorithm Based on Graph Representation for Community Detection
- Acta Electronica Sinica Vol. 48, Issue 9, Pages: 1680-1687(2020)
- 作者机构：
  
  1. 计算智能与信号处理教育部重点实验室,安徽,合肥,230601
  2. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  3. 计算智能与信号处理教育部重点实验室,安徽,合肥,230601
  4. 安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.09.003
  CLC： TP391
- Published Online：25 September 2020，
  
  Published：2020
- 稿件说明：
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CHEN Jie, LI Rui, ZHAO Shu, et al. A New Clustering Cover Algorithm Based on Graph Representation for Community Detection[J]. Acta Electronica Sinica, 2020, 48(9): 1680-1687.
DOI：

CHEN Jie, LI Rui, ZHAO Shu, et al. A New Clustering Cover Algorithm Based on Graph Representation for Community Detection[J]. Acta Electronica Sinica, 2020, 48(9): 1680-1687. DOI： 10.3969/j.issn.0372-2112.2020.09.003.

摘要

图表示社区检测使用图表示方法学习网络节点的向量表示，然后对节点向量进行聚类获得社团结构.然而经典的聚类算法在聚类节点向量时，得到的结果往往不能够体现社区的特性.提出一种新型的聚类覆盖算法，将聚类所得覆盖视为社区划分结果.首先在节点向量空间中计算得到每个簇的覆盖中心；然后根据覆盖中心到同类样本的平均距离作为覆盖半径，在向量空间中形成覆盖；最后对未覆盖的点做二次划分得到社区结构.在多个有真实和无真实标签网络的实验表明，所提出的算法可以得到更合理的社区结果.

Abstract

Community detection based on graph representation learn nodes' vector representation

and then communities are obtained by clustering algorithm. However

when classical clustering algorithms often fail to reflect the characteristics of communities. Cluster cover algorithm (CCL) is proposed. CCL clusters nodes' vector into covers. A cover is viewed as a community. Firstly

the cover center of each cluster is calculated in the node vector space. Then

according to the average distance among the cover center and the same class samples as the cover radius

a cover is formed in the vector space. Finally

the nodes outside the covers are grouped into suitable cover to obtain community structure. Experiments with real and non-real tag networks show that the algorithm can get more reasonable community results.

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