金融网络频繁链路发现算法

随着各种非法金融活动的泛滥，从金融网络中发现犯罪线索的分析研究越来越引起学者的重视。对银行账户交易数据的特点进行了详细分析，建立了银行账户交易网络通用模型。在此基础上，为解决金融实体之间关系强度的评估问题，提出了双向活跃边搜索计算方法。为了还原犯罪组织的资金流动方式，提出了深度可控的广度优先频繁链路发现方法。在真实银行数据上的实验证明，上述方法能有效解决同伙预测和资金追踪问题。

Frequent path discovery algorithm for financial network

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.