模式识别与人工智能
Tuesday, Apr. 22, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2015, Vol. 28 Issue (6): 550-557    DOI: 10.16451/j.cnki.issn1003-6059.201506010
Researches and Applications Current Issue| Next Issue| Archive| Adv Search |
Top-K Relative Community Query Method for Social Network
LI Zhi-Chao, CHEN Hua-Hui, QIAN Jiang-Bo, DONG Yi-Hong
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315000

Download: PDF (469 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  To find top-K relative communities associated with the query point is of significance in practical research . In this paper, the concept of clique and relative community is defined, and a method to rapidly detect the top-K relative communities is explored. A down detection expansion algorithm is proposed. All the clique structures are detected from query point. By extending each clique structure outward to construct a community, the top-K relative communities of the query point is quickly acquired through loop iteration. Meanwhile, to reduce the searching space and computing time, the down detection expansion algorithm is improved. Through comprehensive experimental comparison, the validity of the original algorithm and the efficiency of improved algorithm is verified.
Key wordsSocial Network      Community Query      Relative Community      Clique Detection     
Received: 09 June 2014     
ZTFLH: TP399  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
LI Zhi-Chao
CHEN Hua-Hui
QIAN Jiang-Bo
DONG Yi-Hong
Cite this article:   
LI Zhi-Chao,CHEN Hua-Hui,QIAN Jiang-Bo等. Top-K Relative Community Query Method for Social Network[J]. , 2015, 28(6): 550-557.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201506010      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2015/V28/I6/550
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn