Improving fraud detection via imbalanced graph structure learning

doi:10.1007/s10994-023-06464-0

CSpace

	Improving fraud detection via imbalanced graph structure learning
	Ren, Lingfei 1,2; Hu, Ruimin 1,2,5; Liu, Yang 3; Li, Dengshi 1,4; Wu, Junhang 1,2; Zang, Yilong 1,2; Hu, Wenyi 1,2
	2023-11-29
发表期刊	MACHINE LEARNING
ISSN	0885-6125
页码	22
摘要	Graph-based fraud detection methods have recently attracted much attention due to the rich relational information of graph-structured data, which may facilitate the detection of fraudsters. However, the GNN-based algorithms may exhibit unsatisfactory performance faced with graph heterophily as the fraudsters usually disguise themselves by deliberately making extensive connections to normal users. In addition to this, the class imbalance problem also causes GNNs to overfit normal users and perform poorly for fraudsters. To address these problems, we propose an Imbalanced Graph Structure Learning framework for fraud detection (IGSL for short). Specifically, nodes are picked with a devised multi-relational class-balanced sampler for mini-batch training. Then, an iterative graph structure learning module is proposed to iteratively construct a global homophilic adjacency matrix in the embedding domain. Further, an anchor node message passing mechanism is proposed to reduce the computational complexity of the constructing homophily adjacency matrix. Extensive experiments on benchmark datasets show that IGSL achieves significantly better performance even when the graph is heavily heterophilic and imbalanced.
关键词	Fraud detection Graph structure learning Homophily Heterophily
DOI	10.1007/s10994-023-06464-0
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001120020800008
出版者	SPRINGER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38470
专题	中国科学院计算技术研究所
通讯作者	Hu, Ruimin
作者单位	1.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan, Peoples R China 2.Wuhan Univ, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Jianghan Univ, Sch Artificial Intelligence, Wuhan, Peoples R China 5.Xidian Univ, Sch Cyber Engn, Xian, Peoples R China
推荐引用方式 GB/T 7714	Ren, Lingfei,Hu, Ruimin,Liu, Yang,et al. Improving fraud detection via imbalanced graph structure learning[J]. MACHINE LEARNING,2023:22.
APA	Ren, Lingfei.,Hu, Ruimin.,Liu, Yang.,Li, Dengshi.,Wu, Junhang.,...&Hu, Wenyi.(2023).Improving fraud detection via imbalanced graph structure learning.MACHINE LEARNING,22.
MLA	Ren, Lingfei,et al."Improving fraud detection via imbalanced graph structure learning".MACHINE LEARNING (2023):22.