Skip to main content

Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13245))

Included in the following conference series:

Abstract

Graph Neural Networks (GNNs) have achieved remarkable successes by utilizing rich interactions in network data. When applied to fraud detection tasks, the scarcity and concealment of fraudsters bring two challenges: class imbalance and label noise. In addition to overfitting problem, they will compromise model performance through the message-passing mechanism of GNNs. For a fraudster in a neighborhood dominated by benign users, its learned representation will be distorted in the aggregation process. Noises will propagate through the topology structure as well. In this paper, we propose a Bi-Level Selection (BLS) algorithm to enhance GNNs under imbalanced and noisy scenarios observed from fraud detection. BLS learns to select instance-level and neighborhood-level valuable nodes via meta gradient of the loss on an unbiased clean validation set. By emphasizing BLS-selected nodes in the model training process, bias towards majority class (benign) and label noises will be suppressed. BLS can be applied on most GNNs with slight modifications. Experimental results on two real-world datasets demonstrate that BLS can significantly improve GNNs performance on graph-based fraud detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR, pp. 9268–9277 (2019)

    Google Scholar 

  2. Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: CIKM, pp. 315–324 (2020)

    Google Scholar 

  3. Kaize, D., Qinghai, Z., Hanghang, T., Huan, L.: Few-shot network anomaly detection via cross-network meta-learning. In: WWW, pp. 2448–2456 (2021)

    Google Scholar 

  4. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)

    Google Scholar 

  5. Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., He, Q.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: WWW, pp. 3168–3177 (2021)

    Google Scholar 

  6. Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: SIGIR, pp. 1569–1572 (2020)

    Google Scholar 

  7. McAuley, J.J., Leskovec, J.: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: WWW, pp. 897–908 (2013)

    Google Scholar 

  8. Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: KDD, pp. 985–994 (2015)

    Google Scholar 

  9. Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: ICML, pp. 4334–4343 (2018)

    Google Scholar 

Download references

Acknowledgement

The research work is supported by the National Natural Science Foundation of China under Grant (No.61976204, 92046003, U1811461). Xiang Ao is also supported by the Project of Youth Innovation Promotion Association CAS, Beijing Nova Program Z201100006820062.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiang Ao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dong, L. et al. (2022). Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-00123-9_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00122-2

  • Online ISBN: 978-3-031-00123-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics