Journal of Guangdong University of Technology ›› 2018, Vol. 35 ›› Issue (02): 46-50.doi: 10.12052/gdutxb.170146

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Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks

Rao Dong-ning1, Wang Jun-xing1, Wei lai2, Wang Ya-li3   

  1. 1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Economics and Finance, The University of Hong Kong, Hong Kong 999077, China;
    3. School of Economics and Management, South China Normal University, Guangzhou 510631, China
  • Received:2017-10-16 Online:2018-03-09 Published:2018-03-13
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Abstract: Effective financial supervision has become a necessary guarantee for sound development of economy. Supervising the whole financial social network effectively would become possible if a set of key nodes which carry all the information flow in the financial social network can be found. The scale of social network is often quite large, so parallel algorithms for large-scale graph processing are necessary. A Pregel-based parallel algorithm for the minimal cut set problem is proposed. The experiment is conducted on Apache Spark platform. All data used in the experiment is from the BoardEx database. Experiment results show that the algorithm has a good performance in large-scale social network graph processing. With this parallel algorithm, minimal cut sets of financial social network graphs can be obtained so that effective financial supervision can be implemented.

Key words: Big data, social network, parallel algorithm, minimal cut set, Apache Spark

CLC Number: 

  • TP182
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