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应用Normal矩阵谱平分法的多社团发现 被引量：6

Detecting communities using spectral bisection method based on Normal matrix
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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。 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.
作者 张燕平 王杨 赵姝
机构地区 安徽大学智能计算与信号处理教育部重点实验室
出处 《计算机工程与应用》 CSCD 北大核心 2010年第27期43-45,共3页 Computer Engineering and Applications
基金 国家重点基础研究发展规划(973)No.2004CB318108 No.2007CB311003 国家自然科学基金No.60675031 安徽省高等学校优秀青年人才基金项目(No.2009SQRZ020ZD) 安徽大学211工程学术创新团队 安徽大学人才队伍建设经费资助~~
关键词 社团结构 Normal矩阵 谱平分法 K-MEANS聚类算法 community structure normal matrix spectral bisection method k-means algorithm
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献13

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二级参考文献32

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共引文献26

同被引文献55

  • 1尹进,胡祥培,郑毅,周子轩.社会化商务中基于经验及推荐的消费者感知信任模糊融合模型[J].中国管理科学,2020,0(1):122-133. 被引量：21
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引证文献6

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计算机工程与应用
2010年 第27期

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