Multi-view graph neural network for fraud detection algorithm

Zhuo CHEN; Miao ZHU; Junwei DU

doi:10.11959/j.issn.1000-436x.2022221

您当前的位置：

首页 >

文章列表页 >

Multi-view graph neural network for fraud detection algorithm

Correspondences | 更新时间：2024-06-05

- Multi-view graph neural network for fraud detection algorithm
- Journal on Communications Vol. 43, Issue 11, Pages: 225-232(2022)
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛 266061
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172249);The National Natural Science Foundation of China(61973180);The National Natural Science Foundation of China(62202253);The Natural Science Foundation of Shandong Province(ZR2021MF092)
- DOI：10.11959/j.issn.1000-436x.2022221
  CLC： TP183
- Online First：2022-11，
  
  Published：25 November 2022
- 稿件说明：
移动端阅览
Zhuo CHEN, Miao ZHU, Junwei DU. Multi-view graph neural network for fraud detection algorithm[J]. Journal on Communications, 2022, 43(11): 225-232.
DOI：

Zhuo CHEN, Miao ZHU, Junwei DU. Multi-view graph neural network for fraud detection algorithm[J]. Journal on Communications, 2022, 43(11): 225-232. DOI： 10.11959/j.issn.1000-436x.2022221.

摘要

针对欺诈检测领域样本标签不平衡、欺诈节点之间缺乏必要连接，导致欺诈检测任务不符合图神经网络同质性假设的问题，提出了基于多视角图神经网络的欺诈检测（MGFD）算法。首先，利用结构无关的编码器对网络中节点进行属性编码，以学习欺诈节点与正常节点之间的差异，使用层次注意力机制对网络中多视角信息进行融合，在学习差异的基础上充分利用网络中不同视角之间的交互信息对节点进行建模；然后，基于数据不平衡比采样子图，依据欺诈节点连接特性构建样本进行分类学习，解决样本标签不平衡的问题；最后，预测标签判别节点是否为欺诈节点。在公开数据集上的实验表明，MGFD算法在基于图的欺诈检测领域检测效果优于对比方法。

Abstract

Aiming at the problem that in the field of fraud detection

imbalance labels and lack of necessary connections between fraud nodes

resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks

multi-view graph neural network for fraud detection (MGFD) algorithm was proposed.First

A structure-independent encoder was used to encode the attributes of nodes in the network to learn the difference between the fraud node and the normal node.The hierarchical attention mechanism was designed to integrate the multi-view information in the network

and made full use of the interaction information between different perspectives in the network to model the nodes on the basis of learning differences.Then

based on the data imbalance ratio sampled subgraph

the sample was constructed according to the connection characteristics of fraud nodes for classification

which solved the problem of imbalance sample labels.Finally

the prediction label was used to identify whether a node is fraudulent.Experiments on real-world datasets have shown that the MGFD algorithm outperforms the comparison method in the field of graph-based fraud detection.

关键词

Keywords

references

朱会娟 , 陈锦富 , 李致远 , 等 . 基于多特征自适应融合的区块链异常交易检测方法 [J ] . 通信学报 , 2021 , 42 ( 5 ): 41 - 50 .

ZHU H J , CHEN J F , LI Z Y , et al . Block-chain abnormal transaction detection method based on adaptive multi-feature fusion [J ] . Journal on Communications , 2021 , 42 ( 5 ): 41 - 50 .

POURHABIBI T , ONG K L , KAM B H , et al . Fraud detection:a systematic literature review of graph-based anomaly detection approaches [J ] . Decision Support Systems , 2020 ,133:113303.

ZHANG G , WU J , YANG J , et al . FRAUDRE:fraud detection dual-resistant to graph inconsistency and imbalance [C ] // Proceedings of IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2021 : 867 - 876 .

WANG D X , LIN J B , CUI P , et al . A semi-supervised graph attentive network for financial fraud detection [C ] // Proceedings of IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2019 : 598 - 607 .

MCAULEY J J , LESKOVEC J . From amateurs to connoisseurs:modeling the evolution of user expertise through online reviews [C ] // Proceedings of the 22nd International Conference on World Wide Web . New York:ACM Press , 2013 : 897 - 908 .

LIU Z , DOU Y , YU P S , et al . Alleviating the inconsistency problem of applying graph neural network to fraud detection [C ] // Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2020 : 1569 - 1572 .

DOU Y T , LIU Z W , SUN L , et al . Enhancing graph neural network-based fraud detectors against camouflaged fraudsters [C ] // Proceedings of the 29th ACM International Conference on Information ＆Knowledge Management . New York:ACM Press , 2020 : 315 - 324 .

陈晋音 , 张敦杰 , 黄国瀚 , 等 . 面向图神经网络的对抗攻击与防御综述 [J ] . 网络与信息安全学报 , 2021 , 7 ( 3 ): 28 .

CHEN J Y , ZHANG D J , HUANG G H , et al . Adversarial attack and defense on graph neural networks:a survey [J ] . Chinese Journal of Network and Information Security , 2021 , 7 ( 3 ): 28 .

MA J , ZHANG D , WANG Y , et al . GraphRAD:a graph-based risky account detection system [C ] // Proceedings of ACM SIGKDD Conference . New York:ACM Press , 2018 :9

BIAN T , XIAO X , XU T , et al . Rumor detection on social media with bi-directional graph convolutional networks [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2020 : 549 - 556 .

LI A , QIN Z , LIU R S , et al . Spam review detection with graph convolutional networks [C ] // Proceedings of the 28th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2019 : 2703 - 2711 .

CHAWLA N V , BOWYER K W , HALL L O , et al . SMOTE:synthetic minority over-sampling technique [J ] . Journal of Artificial Intelligence Research , 2002 , 16 : 321 - 357 .

WANG W , WANG S , FAN W , et al . Global-and-local aware data generation for the class imbalance problem [C ] // Proceedings of the 2020 SIAM International Conference on Data Mining . Philadelphia:Society for Industrial and Applied Mathematics , 2020 : 307 - 315 .

CHI J F , ZENG G X , ZHONG Q W , et al . Learning to undersampling for class imbalanced credit risk forecasting [C ] // Proceedings of IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2020 : 72 - 81 .

CAO K , WEI C , GAIDON A , et al . Learning imbalanced datasets with label-distribution-aware margin loss [C ] // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Massachusetts:MIT Press , 2019 : 1567 - 1578 .

HU Z , TAN B , SALAKHUTDINOV R R , et al . Learning data manipulation for augmentation and weighting [C ] // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Massachusetts:MIT Press , 2019 :32.

SHI M , TANG Y F , ZHU X Q , et al . Multi-class imbalanced graph convolutional network learning [C ] // Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence . San Francisco:Margan Kaufmann , 2020 : 2879 - 2885 .

ZHAO T X , ZHANG X , WANG S H . GraphSMOTE:imbalanced node classification on graphs with graph neural networks [C ] // Proceedings of the 14th ACM International Conference on Web Search and Data Mining . New York:ACM Press , 2021 : 833 - 841 .

TONG H H , FALOUTSOS C , PAN J Y . Fast random walk with restart and its applications [C ] // Proceedings of the Sixth International Conference on Data Mining . Piscataway:IEEE Press , 2006 : 613 - 622 .

RAYANA S , AKOGLU L . Collective opinion spam detection:bridging review networks and metadata [C ] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2015 : 985 - 994 .

WELLING M , KIPF T N . Semi-supervised classification with graph convolutional networks [J ] . arXiv Preprint,arXiv:1609.02907 , 2016 .

WANG J Y , WEN R , WU C M , et al . FDGars:fraudster detection via graph convolutional networks in online APP review system [C ] // Proceedings of the 22nd International Conference on World Wide Web . New York:ACM Press , 2019 : 310 - 316 .

Views

1580

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Illicit data resale detection method via an enhanced relational graph convolutional network

Review of unsupervised anomaly detection techniques for medical imaging

Network traffic anomaly detection method based on multi-scale convolution and channel attention mechanism

Trajectory reconstruction attacks on differential privacy based on a CNN-BiLSTM-Attention hybrid model

Anomaly detection method for intelligent UAV networks based on collaborative adversarial enhanced generative model

Related Author

Wang Yuxiang

Zhang Lingcui

Hou Yuqiao

Yang Qian

Niu Ben

Zhao Yingcheng

Song Xiaogang

Shi Zhenghao

Related Institution

Institute of Information Engineering, Chinese Academy of Sciences

School of Cyber Security, University of Chinese Academy of Sciences

State Key Laboratory of Cyberspace Security Defense

School of Computer Science and Engineering, Xi’an University of Technology

Shaanxi Key Laboratory for Network Computing and Security Technology

AI问答

⁰