期刊文献+

识别稳定的局部社区结构算法

Algorithm of identifying stabile local community structure
下载PDF
导出
摘要 通过提取出具有高链接密度的局部团,从局部团出发进行扩展社区,设计自动选择社区扩展的终止条件,以保留最优社区结构,提出一种社会网络局部社区识别算法。在人工生成网络和真实网络上的实验结果表明,与同类算法相比,该算法能够识别出稳定的局部社区结构,提升了局部社区识别结果的准确率。 The local clique with high link density is extracted,the community is extended from the local clique,the termination conditions for automatic selecting community expansion are designed to retain the optimal community structure,and an algorithm for identifying local community of social network is proposed. The experimental results for the artificial network and real network show that compared with the existing algorithm,the given algorithm can identify the stabile local community structure and enhance the accuracy of the local community identification results.
作者 赵泳鑫 钟诚
出处 《信息技术》 2016年第3期19-23,27,共6页 Information Technology
基金 广西自然科学基金(2014GXNSFAA118396) 广西研究生教育创新计划项目(YCSZ2014034)
关键词 社会网络 局部社区识别 局部团 局部链接密度 social network local community identification local clique local link density
  • 相关文献

参考文献12

  • 1Newman M E J,Girvan M.Finding and evaluating community structure in networks[J].Physical review E,2004,69(2):026113.
  • 2Xu X,Yuruk N,Feng Z,et al.Scan:a structural clustering algorithm for networks[C]∥Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.New York:ACM,2007:824-833.
  • 3Clauset A,Newman M E J,Moore C.Finding community structure in very large networks[J].Physical review E,2004,70(6):066111.
  • 4Bagrow J P,Bollt E M.Local method for detecting communities[J].Physical Review E,2005,72(4):046108.
  • 5Clauset A.Finding local community structure in networks[J].Physical review E,2005,72(2):026132.
  • 6Chen J,Zaane O,Goebel R.Local community identification in social networks[C]∥Proceedings of 2009 International Conference on Advances in Social Network Analysis and Mining.Piscataway,NJ:IEEE,2009:237-242.
  • 7Xu J.Identifying Cohesive Local Community Structures in Networks[C]∥Proceedings of the 30th International Conference on information Systems,Phoenix,Arizona,USA,Dec.15-18,2009:112.
  • 8Andersen R.A local algorithm for finding dense subgraphs[J].ACM Transactions on Algorithms,2010,6(4):60.
  • 9Duch J,Arenas A.Community detection in complex networks using extremal optimization[J].Physical review E,2005,72(2):027104.
  • 10Newman M E J.Finding community structure in networks using the eigen vectors of matrices[J].Physical review E,2006,74(3):036104.

二级参考文献33

  • 1Parthasarathy S, Ruan Y, Satuluri V. Community discov- ery in social networks: Applications, methods and emerging trends. In Social Network Data Analyties, Aggarwal C (ed.), 2011, pp.79-113.
  • 2Tang W, Zhuang H, Tang J. Learning to infer social ties in large networks. In Proc. the 2011 European Conf. Machine Learning and Knowledge Discovery in Databases, Sept. 2011, Part 3, pp.381-397.
  • 3Kumar R, Raghavan P, Rajagopalan S, Sivakumar D, Tomp- kins A, Upfal E. The web as a graph. In Proc. the 19th A CM 3IGMOD-SIGA CT-SIGART Symposium on Principles of Database Systems, May 2000, pp.1-10.
  • 4Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z. Arnetminer: Ex- traction and mining of academic social networks. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2008, pp.990-998.
  • 5Clauset A, Newman M, Moore C. Finding community struc- ture in very large networks. Physical review E, 2004, 70(6): 066111.
  • 6Rattigan M, Maier M, Jensen D. Graph clustering with net- work structure indices. In Proc. the 24th International Con- ference on Machine Learning, June 2007, pp.783-790.
  • 7Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing, 2007, 17(4): 395-416.
  • 8Pan J J, Yang Q. Co-localization from labeled and unlabeled data using graph laplacian. In Proc. the 20th Int. Joint Conf. Artificial Intelligence, Jan. 2007, pp.2166-2171.
  • 9Fortunato S. Community detection in graphs. Physics Re- ports, 2010, 486(3/5): 75-174.
  • 10Su Z, Yang Q, Zhang H, Xu X, Hu Y. Correlation-based doc- ument clustering using web logs. In Proe. the 34th Annual Hawaii Int. Conf. System Sciences, 2001, Vol.5,, p.5022.

共引文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部