FraudNE: a joint embedding approach for fraud detection

Mengyu Zheng, Chuan Zhou, Jia Wu, Shirui Pan, Jinqiao Shi, And Li Guo

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


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 languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks (IJCNN) - 2018 Proceedings
EditorsTeresa Ludermir
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2018
Abbreviated titleIJCNN 2018
CityRio de Janeiro
Internet address


  • Deep Structure Learning
  • Dense Block Detection
  • Network Embedding

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