Abstract
Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement.
| Original language | English |
|---|---|
| Title of host publication | 2018 International Joint Conference on Neural Networks (IJCNN) - 2018 Proceedings |
| Editors | Teresa Ludermir |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 4739-4746 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509060146 |
| ISBN (Print) | 9781509060153 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | IEEE International Joint Conference on Neural Networks 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 Conference number: 2018 http://www.ecomp.poli.br/~wcci2018/ |
Conference
| Conference | IEEE International Joint Conference on Neural Networks 2018 |
|---|---|
| Abbreviated title | IJCNN 2018 |
| Country | Brazil |
| City | Rio de Janeiro |
| Period | 8/07/18 → 13/07/18 |
| Internet address |
Keywords
- Deep Structure Learning
- Dense Block Detection
- Network Embedding
Cite this
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FraudNE : a joint embedding approach for fraud detection. / Zheng, Mengyu; Zhou, Chuan; Wu, Jia; Pan, Shirui; Shi, Jinqiao; Guo, And Li.
2018 International Joint Conference on Neural Networks (IJCNN) - 2018 Proceedings. ed. / Teresa Ludermir. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 4739-4746 8489585.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - FraudNE
T2 - a joint embedding approach for fraud detection
AU - Zheng, Mengyu
AU - Zhou, Chuan
AU - Wu, Jia
AU - Pan, Shirui
AU - Shi, Jinqiao
AU - Guo, And Li
PY - 2018
Y1 - 2018
N2 - Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement.
AB - Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement.
KW - Deep Structure Learning
KW - Dense Block Detection
KW - Network Embedding
UR - http://www.scopus.com/inward/record.url?scp=85056514379&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489585
DO - 10.1109/IJCNN.2018.8489585
M3 - Conference Paper
SN - 9781509060153
SP - 4739
EP - 4746
BT - 2018 International Joint Conference on Neural Networks (IJCNN) - 2018 Proceedings
A2 - Ludermir, Teresa
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -