Detecting communities using spectral bisection method based on Normal matrix

doi:10.3778/j.issn.1002-8331.2010.27.011

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (27): 43-45.DOI: 10.3778/j.issn.1002-8331.2010.27.011

• 研究、探讨 • Previous Articles Next Articles

Detecting communities using spectral bisection method based on Normal matrix

ZHANG Yan-ping，WANG Yang，ZHAO Shu

MOE Key Lab of Intelligent Computing & Signal Processing，Anhui University，Hefei 230039，China

Received:2009-03-12 Revised:2009-05-11 Online:2010-09-21 Published:2010-09-21
Contact: ZHANG Yan-ping

应用Normal矩阵谱平分法的多社团发现

张燕平，王杨，赵姝

安徽大学智能计算与信号处理教育部重点实验室，合肥 230039

通讯作者: 张燕平

Abstract

Abstract: Community structure is a common property that exists in complex networks.Detecting communities is important for understanding network structure and analyzing the network characteristics.The characteristics of common community finding algorithm and the drawback of spectral bisection method in application are analyzed.The method of multi-community finding in complex networks using the spectral bisection method based on normal matrix is provided.This algorithm can select the appropriate number of eigenvector dimension and provide effective data for k-means algorithm.Compared to other algorithms this algorithm has higher accuracy.

Key words: community structure, normal matrix, spectral bisection method, k-means algorithm

摘要：

现实世界中许多实际网络都有一个共同的性质，即社团结构。揭示网络中的社团结构，对于了解网络结构与分析网络性质都是很重要的。分析了常见的社团发现算法的特点，以及谱二分法在实际应用中必须不断迭代才能完成多社团发现的不足，提出了基于Normal矩阵和k-means聚类算法的多社团发现方法。该算法能选择合适的特征向量维数，为k-means划分社团提供有效数据，相比其他算法有着较高的准确率。

关键词: 社团结构, Normal矩阵, 谱平分法, k-means聚类算法

CLC Number:

TP393

ZHANG Yan-ping，WANG Yang，ZHAO Shu. Detecting communities using spectral bisection method based on Normal matrix[J]. Computer Engineering and Applications, 2010, 46(27): 43-45.

张燕平，王杨，赵姝. 应用Normal矩阵谱平分法的多社团发现[J]. 计算机工程与应用, 2010, 46(27): 43-45.

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Detecting communities using spectral bisection method based on Normal matrix

应用Normal矩阵谱平分法的多社团发现

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