Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning
Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph represen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/14/3070 |
| _version_ | 1797589500696723456 |
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| author | Xuxu Zheng Chen Feng Zhiyi Yin Jinli Zhang Huawei Shen |
| author_facet | Xuxu Zheng Chen Feng Zhiyi Yin Jinli Zhang Huawei Shen |
| author_sort | Xuxu Zheng |
| collection | DOAJ |
| description | Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy. |
| first_indexed | 2024-03-11T01:07:27Z |
| format | Article |
| id | doaj.art-2262350aae2a41b3a227ef3080aa9635 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:07:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2262350aae2a41b3a227ef3080aa96352023-11-18T19:05:24ZengMDPI AGElectronics2079-92922023-07-011214307010.3390/electronics12143070Research on Fraud Detection Method Based on Heterogeneous Graph Representation LearningXuxu Zheng0Chen Feng1Zhiyi Yin2Jinli Zhang3Huawei Shen4Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaData Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaData Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100086, ChinaDetecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy.https://www.mdpi.com/2079-9292/12/14/3070heterogeneousinformation networkfraud detectiongraph convolutional networkssimilarity |
| spellingShingle | Xuxu Zheng Chen Feng Zhiyi Yin Jinli Zhang Huawei Shen Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning Electronics heterogeneous information network fraud detection graph convolutional networks similarity |
| title | Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning |
| title_full | Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning |
| title_fullStr | Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning |
| title_full_unstemmed | Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning |
| title_short | Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning |
| title_sort | research on fraud detection method based on heterogeneous graph representation learning |
| topic | heterogeneous information network fraud detection graph convolutional networks similarity |
| url | https://www.mdpi.com/2079-9292/12/14/3070 |
| work_keys_str_mv | AT xuxuzheng researchonfrauddetectionmethodbasedonheterogeneousgraphrepresentationlearning AT chenfeng researchonfrauddetectionmethodbasedonheterogeneousgraphrepresentationlearning AT zhiyiyin researchonfrauddetectionmethodbasedonheterogeneousgraphrepresentationlearning AT jinlizhang researchonfrauddetectionmethodbasedonheterogeneousgraphrepresentationlearning AT huaweishen researchonfrauddetectionmethodbasedonheterogeneousgraphrepresentationlearning |