Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (5): 55-62.doi: 10.16180/j.cnki.issn1007-7820.2019.05.011

Previous Articles     Next Articles

Link Prediction on Bank Transaction Network

MA Qingqing,YAN Guanghui,WANG Yafei,WU Yu   

  1. School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2018-05-04 Online:2019-05-15 Published:2019-05-06
  • Supported by:
    National Natural Science Foundation of China(61662066)


Based on the dynamic changes in bank transactions and the characteristics of timeliness and repeatability, the basic topology statistical properties and clustering structure of the bank's network were studied, and obtained the transaction network satisfied with the small-world and scale-free characteristics.Based on the deficiency of existing link prediction algorithms in dynamic network prediction, a new dynamic link algorithm was proposed to predict bank customer transactions. Then, based on the algorithm mentioned above, two characteristics, the three predictive algorithms combined with the random algorithm were compared. These three algorithms were applied to the three types of real data sets with dynamic transaction characteristics for experimental verification. The results showed that the prediction accuracy of the algorithm was about 75%. Finally, comparing the algorithm with the classical prediction algorithm, the proposed algorithm improved the prediction by 5% to 10%.

Key words: complex network, link prediction, bank network, node centrality, strength and weakness, accuracy

CLC Number: 

  • TP391