中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Improving fraud detection via imbalanced graph structure learning

文献类型：期刊论文

作者Ren, Lingfei4,5; Hu, Ruimin1,4,5; Liu, Yang3; Li, Dengshi2,5; Wu, Junhang4,5; Zang, Yilong4,5; Hu, Wenyi4,5
刊名MACHINE LEARNING
出版日期2023-11-29
页码22
关键词Fraud detection Graph structure learning Homophily Heterophily
ISSN号0885-6125
DOI10.1007/s10994-023-06464-0
英文摘要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.
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001120020800008
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/38469]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Ruimin
作者单位1.Xidian Univ, Sch Cyber Engn, Xian, Peoples R China
2.Jianghan Univ, Sch Artificial Intelligence, Wuhan, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
4.Wuhan Univ, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan, Peoples R China
5.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp Sci, Wuhan, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。