Parallel Knowledge Community Detection Algorithm Research Based on MapReduce


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Knowledge community detection algorithm is a main technical means to knowledge discovery in complex network. It is used to discover the potential community structures to provide a basis for web services, for example, personalized recommendations. In this paper, a parallel algorithm for knowledge community detection based on MapReduce is proposed. This paper, through various experiments, proves that the efficiency of the parallel algorithm is increased significantly.



Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin




M. Xu et al., "Parallel Knowledge Community Detection Algorithm Research Based on MapReduce", Advanced Materials Research, Vols. 204-210, pp. 1646-1650, 2011

Online since:

February 2011




[1] Han Yan.Data Mining and Knowledge Discovery [J].Computer Knowledge and Technology(Academic Exchange),2007,02(08):513~514.

[2] Scott J. Social Network Analysis: A Handbook [M]. Sage Publications, London, (2000).

[3] Wang Rong. Social Group Collaborative Learning and Knowledge Sharing – Research on the Mechanism and Key Technologies in Web Environment:[PhD thesis].Beijing:Renmin University of China,(2009).

[4] Yang H C, Dasdan A, Hsiao R L, et al1. Map - Reduce - Merge: Simplified Relational Data Processing on Large Clusters [ C ] International Conference on Management of Data Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data,p.1029.

DOI: 10.1145/1247480.1247602

[5] Jeffrey Dean and Sanjay Ghemawat. MapReduce: A Flexible Data Processing Tool. Communications of the ACM , January 2010 , vol. 53 , no. 1. pp.72-75.

DOI: 10.1145/1629175.1629198

[6] Min Xu, Meiqi Fang and Ning Li: An Intelligent Personalized Learning Model Based on Community Discovery Method. Proceedings of the international conference on Micro/Nano Devices, Structures and Systems, 2010. 11.

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