Frequent path discovery algorithm for financial network - Fang LYU; Fenghe TANG; Junheng HUANG; Bailing WANG (网络与信息安全学报)
With the proliferation of various illegal financial activities,more and more attention is paid to the research of finding criminal cues in financial network by scholars.The characteristics of the transaction data generated by bank accounts are analyzed in detail,and a general model of bank account transaction network is established.On this basis,a two-direction active edge searching method is proposed to solve the problem of evaluating the relationship strength between financial entities.And then,a breadth-first frequent path discovery algorithm with depth controlled is presented,with which the way how the financial flows is restored.Experiment results on the real bank data show that the above two methods are effective in solving the problem of peer prediction and financial tracking respectively.