Multi-view graph neural network for fraud detection algorithm
Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view graph neural network for fraud detection (MGFD)...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022221/ |
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| _version_ | 1841539964762849280 |
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| author | Zhuo CHEN Miao ZHU Junwei DU |
| author_facet | Zhuo CHEN Miao ZHU Junwei DU |
| author_sort | Zhuo CHEN |
| collection | DOAJ |
| description | Aiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view graph neural network for fraud detection (MGFD) algorithm was proposed.First, A structure-independent encoder was used to encode the attributes of nodes in the network to learn the difference between the fraud node and the normal node.The hierarchical attention mechanism was designed to integrate the multi-view information in the network, and made full use of the interaction information between different perspectives in the network to model the nodes on the basis of learning differences.Then, based on the data imbalance ratio sampled subgraph, the sample was constructed according to the connection characteristics of fraud nodes for classification, which solved the problem of imbalance sample labels.Finally, the prediction label was used to identify whether a node is fraudulent.Experiments on real-world datasets have shown that the MGFD algorithm outperforms the comparison method in the field of graph-based fraud detection. |
| format | Article |
| id | doaj-art-3b1629ea3cdb4dbb80c9ff4f3c3ee639 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3b1629ea3cdb4dbb80c9ff4f3c3ee6392025-01-14T06:29:24ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-11-014322523259393974Multi-view graph neural network for fraud detection algorithmZhuo CHENMiao ZHUJunwei DUAiming at the problem that in the field of fraud detection, imbalance labels and lack of necessary connections between fraud nodes, resulting in fraud detection tasks not conforming to the hypothesis of homogeneity of graph neural networks, multi-view graph neural network for fraud detection (MGFD) algorithm was proposed.First, A structure-independent encoder was used to encode the attributes of nodes in the network to learn the difference between the fraud node and the normal node.The hierarchical attention mechanism was designed to integrate the multi-view information in the network, and made full use of the interaction information between different perspectives in the network to model the nodes on the basis of learning differences.Then, based on the data imbalance ratio sampled subgraph, the sample was constructed according to the connection characteristics of fraud nodes for classification, which solved the problem of imbalance sample labels.Finally, the prediction label was used to identify whether a node is fraudulent.Experiments on real-world datasets have shown that the MGFD algorithm outperforms the comparison method in the field of graph-based fraud detection.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022221/fraud detectionanomaly detectionattention mechanismgraph representation learningimbalance learning |
| spellingShingle | Zhuo CHEN Miao ZHU Junwei DU Multi-view graph neural network for fraud detection algorithm Tongxin xuebao fraud detection anomaly detection attention mechanism graph representation learning imbalance learning |
| title | Multi-view graph neural network for fraud detection algorithm |
| title_full | Multi-view graph neural network for fraud detection algorithm |
| title_fullStr | Multi-view graph neural network for fraud detection algorithm |
| title_full_unstemmed | Multi-view graph neural network for fraud detection algorithm |
| title_short | Multi-view graph neural network for fraud detection algorithm |
| title_sort | multi view graph neural network for fraud detection algorithm |
| topic | fraud detection anomaly detection attention mechanism graph representation learning imbalance learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022221/ |
| work_keys_str_mv | AT zhuochen multiviewgraphneuralnetworkforfrauddetectionalgorithm AT miaozhu multiviewgraphneuralnetworkforfrauddetectionalgorithm AT junweidu multiviewgraphneuralnetworkforfrauddetectionalgorithm |