Deep structure learning for fraud detection

Haibo Wang, Chuan Zhou*, Jia Wu, Weizhen Dang, Xingquan Zhu, Jilong Wang

*Corresponding author for this work

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

23 Citations (Scopus)


Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781538691588, 9781538691595
ISBN (Print)9781538691601
Publication statusPublished - 27 Dec 2018
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018


Conference18th IEEE International Conference on Data Mining, ICDM 2018


  • Behavior similarity
  • Density block
  • Fraud detection
  • Graph structure learning


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