JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (1): 106-114.doi: 10.6040/j.issn.1671-9352.1.2015.121

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A community mining algorithm based on core nodes expansion

LIU Jing-lian1,2, WANG Da-ling1,3, ZHAO Wei-ji2, FENG Shi1,3, ZHANG Yi-fei1,3   

  1. 1. School of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China;
    2. Information Engineering College, Suihua University, Suihua 152061, Heilongjiang, China;
    3. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110819, Liaoning, China
  • Received:2015-08-10 Online:2016-01-16 Published:2016-11-29

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Abstract: Based on the connections between the nodes in a network and the influence of each node, a community mining algorithm based on expansion of core nodes was proposed in this paper. The algorithm was divided into three stages. Firstly, the top-k core nodes were expanded to their neighbors until most nodes were covered in the network, that the core nodes and its neighbors formed candidate initial communities. Secondly, candidate initial communities were overlap processed, the overlap degree of each candidate initial communities pair was calculated, and the small candidate initial community was deleted if the overlap degree is greater than a given threshold. The left communities formed the initial communities. Finally, the connection degree for the overlap nodes between the initial communities and the nodes outside the communities to each candidate initial communitiy was calculated, and the node with maximum connection degree was added to the corresponding initial community. These operations were repeated until all nodes were assigned to the respective community. Experiment results showed the effectiveness and flexibility of the proposed algorithm in this paper. Compared with the GN and FN algorithm, our algorithm could divide the network accurately. Due to the overlap operation of the candidate initial communities, our algorithm was not sensitive within a certain range compared with Hub and TopLeaders algorithms.

Key words: core nodes, community structure, connection degree

CLC Number: 

  • TP399
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