吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (02): 444-450.

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Discovering tribe-leaders based on frequent pattern of propagation

LIU Li-na1, SHEN Ji-hong1,2, ZHU Qiang-hua3, DING Zhao-yun4   

  1. 1. College of Automation, Harbin Engineering University, Harbin 150001, China;
    2. College of Science, Harbin Engineering University, Harbin 150001, China;
    3. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China;
    4. College of Computer, National University of Defense Technology, Changsha 410073, China
  • Received:2012-06-05 Online:2013-03-01 Published:2013-03-01

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Abstract: A novel scheme of mining tribe-leaders was proposed based on the frequent pattern of propagation. In this scheme, first, a method to expend the information tree is applied to overcome the problem of multi-pattern propagation, in which the information propagation tree is converted into a connected and undirected acyclic graph. Then, considering the support and influent strength, a new frequent sub-graph mining method called Tribe-FGM is proposed to improve the efficiency of the graph mining by reducing the scale of pattern growth. A real dataset from sina microblog was taken in the experiment. The dataset is about topic of "earthquake", which contains 0.9 million posts and 0.6 million users, and the topic of the "two sessions", which contains about 0.31 million posts and 0.21 users. Experiment results validate the proposed scheme.

Key words: computer application, social network, frequent pattern, influence

CLC Number: 

  • TP391
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