Improving fraud detection via hierarchical attention-based Graph Neural Network
文献类型：期刊论文
| 作者 | Liu, Yajing2; Sun, Zhengya1 ; Zhang, Wensheng1
|
| 刊名 | JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 72页码:10 |
| 关键词 | Graph Neural Networks Fraud detection Attention mechanism |
| ISSN号 | 2214-2126 |
| DOI | 10.1016/j.jisa.2022.103399 |
| 通讯作者 | Sun, Zhengya(zhengya.sun@ia.ac.cn) |
| 英文摘要 | 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. |
| 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000909640200001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key R&D Pro-gram of China ; National Natural Science Foundation of China ; Natural Science Founda-tion of Beijing Municipality |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51117]  |
| 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.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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