金融网络频繁链路发现算法

Fang Lyu; Fenghe Tang; Junheng Huang; Bailing Wang

doi:10.11959/j.issn.2096-109x.2019050

金融网络频繁链路发现算法

Translated title of the contribution: Frequent path discovery algorithm for financial network

Fang Lyu
, Fenghe Tang
, Junheng Huang
, Bailing Wang^*

^*Corresponding author for this work

School of Computer Science and Technology (School of Software), Harbin Institute of Technology Weihai

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Translated title of the contribution	Frequent path discovery algorithm for financial network
Original language	Chinese (Traditional)
Article number	2019050
Pages (from-to)	48-55
Number of pages	8
Journal	Chinese Journal of Network and Information Security
Volume	5
Issue number	5
DOIs	https://doi.org/10.11959/j.issn.2096-109x.2019050
State	Published - Oct 2019
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

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10.11959/j.issn.2096-109x.2019050

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title = "金融网络频繁链路发现算法",

abstract = "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.",

keywords = "financial tracking, frequent path, peer prediction, two-direction active edge",

author = "Fang Lyu and Fenghe Tang and Junheng Huang and Bailing Wang",

year = "2019",

month = oct,

doi = "10.11959/j.issn.2096-109x.2019050",

language = "繁体中文",

volume = "5",

pages = "48--55",

journal = "Chinese Journal of Network and Information Security",

issn = "2096-109X",

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TY - JOUR

T1 - 金融网络频繁链路发现算法

AU - Lyu, Fang

AU - Tang, Fenghe

AU - Huang, Junheng

AU - Wang, Bailing

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

KW - financial tracking

KW - frequent path

KW - peer prediction

KW - two-direction active edge

UR - https://www.scopus.com/pages/publications/85133460661

U2 - 10.11959/j.issn.2096-109x.2019050

DO - 10.11959/j.issn.2096-109x.2019050

M3 - 文章

AN - SCOPUS:85133460661

SN - 2096-109X

VL - 5

SP - 48

EP - 55

JO - Chinese Journal of Network and Information Security

JF - Chinese Journal of Network and Information Security

IS - 5

M1 - 2019050

ER -

金融网络频繁链路发现算法

Abstract

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