Improving fraud detection via hierarchical attention-based Graph Neural Network

doi:10.1016/j.jisa.2022.103399

	Improving fraud detection via hierarchical attention-based Graph Neural Network
	Liu, Yajing 1; Sun, Zhengya2 ; Zhang, Wensheng2
发表期刊	JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
ISSN	2214-2126
	2023-02-01
卷号	72 页码:10
通讯作者	Sun, Zhengya(zhengya.sun@ia.ac.cn)
摘要	Fraud has seriously influenced the social media ecosystems, and malicious users pursue high profit by disseminating fake information. Graph neural networks (GNN) have shown a promising potential for fraud detection tasks, where fraudulent nodes are identified by aggregating the neighbors that share similar feedbacks and relations. However, crafty fraudsters can trivially get around such detection via seemingly legitimate feedbacks once connected to legitimate users. In this paper, we leverage Relational Density Theory and propose a Hierarchical Attention-based Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted adjacency matrices across different relations against camouflage. This is motivated by the fact that there are dense connections between fraudsters who collectively participate in fraud activities. Specifically, we design a relation attention module to reflect the tie strength between two nodes, while a neighborhood attention module to capture the long-range structural affinity associated with the graph. We generate node embeddings by aggregating information from local/long-range structures and original node features. Experiments on three real-world datasets demonstrate that our approach achieves 3.21 - 9.97% RUC improvement compared with the state-of-the-arts.
关键词	Graph Neural Networks Fraud detection Attention mechanism
DOI	10.1016/j.jisa.2022.103399
收录类别	SCI
语种	英语
资助项目	National Key R&D Pro-gram of China ; National Natural Science Foundation of China ; Natural Science Founda-tion of Beijing Municipality ; [2017YFC0803700] ; [61876183] ; [4172063]
项目资助者	National Key R&D Pro-gram of China ; National Natural Science Foundation of China ; Natural Science Founda-tion of Beijing Municipality
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000909640200001
出版者	ELSEVIER
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51118
专题	多模态人工智能系统全国重点实验室
通讯作者	Sun, Zhengya
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China
通讯作者单位	精密感知与控制研究中心
推荐引用方式 GB/T 7714	Liu, Yajing,Sun, Zhengya,Zhang, Wensheng. Improving fraud detection via hierarchical attention-based Graph Neural Network[J]. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS,2023,72:10.
APA	Liu, Yajing,Sun, Zhengya,&Zhang, Wensheng.(2023).Improving fraud detection via hierarchical attention-based Graph Neural Network.JOURNAL OF INFORMATION SECURITY AND APPLICATIONS,72,10.
MLA	Liu, Yajing,et al."Improving fraud detection via hierarchical attention-based Graph Neural Network".JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 72(2023):10.