Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (4): 47-49.DOI: 10.3778/j.issn.1002-8331.2011.04.013

• 研究、探讨 • Previous Articles     Next Articles

Classification method in complex networks based on Semi-supervising

KONG Jian1,XIE Fuding1,SUN Yan1,ZHAO Fengxia2   

  1. 1.College of Computer and Information,Liaoning Normal University,Dalian,Liaoning 116081,China
    2.Qinhuangdao Vocational College,Qinhuangdao,Hebei 066100,China
  • Received:2009-06-08 Revised:2009-07-28 Online:2011-02-01 Published:2011-02-01
  • Contact: KONG Jian


孔 健1,谢福鼎1,孙 岩1,赵凤霞2   

  1. 1.辽宁师范大学 计算机与信息学院,辽宁 大连 116081
    2.秦皇岛职业技术学院,河北 秦皇岛 066100
  • 通讯作者: 孔 健

Abstract: A method for identifying the community structure is proposed in this paper based on semi-supervised model.By introducing the Gravity,the communities structure is detected by calculating relationship between the labeled nodes and the unlabeled nodes.The results of experiment prove that the algorithm can identify the community structure in some real-networks.

Key words: complex networks, community structure, semi-supervised classification

摘要: 提出了一种基于半监督模式的复杂网络社团划分新方法,通过引入物理学中的万有引力定理进而计算出有标签节点与周围无标签节点的相互作用值,最终将网络中的社团划分出来。实验表明,算法可以比较准确地划分出一些网络中的社团结构。

关键词: 复杂网络, 社团结构, 半监督分类

CLC Number: