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 |
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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 http://www.ecomp.poli.br/~wcci2018/ https://ieeexplore.ieee.org/xpl/conhome/8465565/proceeding (Proceedings) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2018 |
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Abbreviated title | IJCNN 2018 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |
Internet address |
Keywords
- Deep Structure Learning
- Dense Block Detection
- Network Embedding