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一种基于极大连通子图的电信社群网分割算法 被引量:2

Algorithm of splitting telecom society network based on maximal connected subgraph
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摘要 随着电信事业的发展,电信社群网的分析逐渐兴起。根据电信数据的特点,以关系数据库为基础,实现了一个极大连通子图求解算法(MCSG)。该算法利用等价类的概念实现了图数据分层处理,利用边标识法表示极大连通子图,确保了结果中顶点和边信息的完整性。实验表明,MCSG算法有效实现了对电信社群网的分割。 With the development of telecommunications,analysis of telecom society network rises gradually.According to the char acteristics of telecom data, an algorithm( MCSG ) of searching maximal connected subgraph was proposed based on relational database.This algorithm realized hierachical processing of graph data with concept of equivalent class.Maximal connected subgraph was expressed with edge identifier pattern,thus,the information integrity of vertices and edges can be insured.From experiment results,algorithm MCSG realized splitting telecom society network effectively.
出处 《计算机工程与应用》 CSCD 北大核心 2008年第5期8-9,13,共3页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60473125) 中国石油(CNPC)石油科技中青年创新基金(CNPC Innovation Fund under Grant No.05E7013)
关键词 电信社群网 极大连通子图 算法 teleeom society network maximal connected subgraph algorithm
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  • 1吴彤.复杂网络研究及其意义[J].哲学研究,2004(8):58-63. 被引量:46
  • 2韦洛霞.复杂网络模型和方法[J].东莞理工学院学报,2004,11(4):17-20. 被引量:4
  • 3周涛,傅忠谦,牛永伟,王达,曾燕,汪秉宏,周佩玲.复杂网络上传播动力学研究综述[J].自然科学进展,2005,15(5):513-518. 被引量:72
  • 4[4]Erdos, Renyi A. On the evolution of random graphs [J]. Publ Math Inst Hung Acad Sci, 1959(5) :17 -60.
  • 5[5]Watts D J, Strogatz S H. Collective dynamics of‘small-world’networks[ J]. Nature, 1998, 393:440 -442.
  • 6[6]B arabasi A L, Albert R. Jeong H,et al. Power-law distribution of the world wide web[ J ]. Science , 2000,287: 2115a.
  • 7[8]Ulrik, Brandes. A faster algorithm for betweenness centrality[ J]. Journal of Mathematical Sociology ,2001,25 (2): 163 - 177.
  • 8Rakesh Agrawal, Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases. VLDB1994, Santiago,Chile, 1994.
  • 9Heikki Mannila, et al. Search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery,1997, 1(3): 241~258.
  • 10Jong Soo Park, et al. An effective Hash based algorithm for mining association rules. SIGMOD1995, San Jose, USA, 1995.

共引文献21

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  • 1邱念慈.n阶无向完全图非同构生成子图的结构与作法[J].扬州教育学院学报,2004,22(3):4-9. 被引量:1
  • 2胡作霆,董兰芳,王洵.图的数据挖掘算法研究[J].计算机工程,2006,32(3):76-78. 被引量:8
  • 3Kuramochi M,Karypis G. Finding frequent patterns in a large sparse graph [J]. Data Mining and Knowledge Discovery, 2005,11(3) :243-271.
  • 4Inokuchi A,Washio T,Motoda H. An apriori-based algorithm for mining frequent substructures from graph data [C]// Djamel A Zighed, Henryk Jan Komorowski,Jan M Zytkow. Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery. London: UK, Springer-Verlag, 2000 : 13-23.
  • 5Agrawal R,Imielinski T,Swanmi A. Mining association rules between sets of items in large database [C]// Proe of the ACM SIGMOD Conference on Management of Data. Washington DC:SIGMOD, 1993,207-216.
  • 6Washio T, Motoda H. State of the Art of Graph-based Data Mining [J]. ACM SIGKDD Explorations Newsletter,2003, 5(1):59-68.
  • 7Akihiro Inokuchi,Takashi Washio, Hiroshi Motoda. Complete Mining of Frequent Patterns from Graphs: Mining Graph Data[J]. Machine Learing,2003,50:321-354.
  • 8Jiawei Han.Michaeline Kamber.数据挖掘概念与技术[M].北京:机械工业出版社,2004
  • 9周旭东,王丽爱,陈崚.启发式算法求解最大团问题研究[J].计算机工程与设计,2007,28(18):4329-4332. 被引量:10
  • 10李玉华,罗汉果,孙小林.一种基于Apriori思想的频繁子图发现算法[J].计算机工程与科学,2007,29(4):84-87. 被引量:5

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