Abstract
Graph fraud detection approaches traditionally present frauds as subgraphs and focus on characteristics of the fraudulent subgraphs: unexpectedly high densities or sparse connections with the rest of the graph. However, frauds can easily circumvent such approaches by manipulating their subgraph density or making connections to honest user groups. We focus on a trait that is hard for fraudsters to manipulate: the unidirectionality of communication between honest users and fraudsters. We define an accessibility score to quantify the unidirectionality, then prove the unidirectionality induces skewed accessibility score distributions for fraudsters. We propose SkewA, a novel fraud detection method that measures the skewness in accessibility score distributions and uses it as an honesty metric. SkewA is (a) robust to frauds with low density and various types of camouflages, (b) theoretically sound: we analyze how the unidirectionality brings skewed accessibility score distributions, and (c) effective: showing up to 95.6% accuracy in real-world data where all competitors fail to detect any fraud.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2014). https://doi.org/10.1007/s10618-014-0365-y
Beutel, A., Xu, W., Guruswami, V., Palow, C., Faloutsos, C.: Copycatch: stopping group attacks by spotting lockstep behavior in social networks. In: Proceedings of the 22nd International Conference on World Wide Web (2013)
Cao, Q., Sirivianos, M., Yang, X., Pregueiro, T.: Aiding the detection of fake accounts in large scale social online services. In: Presented as part of the 9th \(\{\)USENIX\(\}\) Symposium on Networked Systems Design and Implementation (\(\{\)NSDI\(\}\) 2012) (2012)
Dhawan, S., Gangireddy, S.C.R., Kumar, S., Chakraborty, T.: Spotting collective behaviour of online frauds in customer reviews. arXiv preprint arXiv:1905.13649 (2019)
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: Fraudar: bounding graph fraud in the face of camouflage. In: KDD (2016)
Jia, J., Wang, B., Gong, N.Z.: Random walk based fake account detection in online social networks. In: 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE (2017)
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Catchsync: catching synchronized behavior in large directed graphs. In: KDD (2014)
Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton (2011)
Liu, S., Hooi, B., Faloutsos, C.: Holoscope: topology-and-spike aware fraud detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (2017)
Pan, J.Y., Yang, H.J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: KDD (2004)
Prakash, B.A., Seshadri, M., Sridharan, A., Machiraju, S., Faloutsos, C.: Eigenspokes: surprising patterns and community structure in large graphs (2010)
Shah, N., Beutel, A., Gallagher, B., Faloutsos, C.: Spotting suspicious link behavior with fbox: an adversarial perspective. In: ICDM (2014)
Tong, H., Lin, C.Y.: Non-negative residual matrix factorization with application to graph anomaly detection. In: SDM (2011)
Wang, B., Gong, N.Z., Fu, H.: Gang: detecting fraudulent users in online social networks via guilt-by-association on directed graphs. In: 2017 IEEE International Conference on Data Mining (ICDM). IEEE (2017)
Wang, B., Zhang, L., Gong, N.Z.: Sybilscar: Sybil detection in online social networks via local rule based propagation. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE (2017)
Yang, C., Harkreader, R., Zhang, J., Shin, S., Gu, G.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: Proceedings of the 21st International Conference on World Wide Web (2012)
Yoon, M., Jung, J., Kang, U.: TPA: fast, scalable, and accurate method for approximate random walk with restart on billion scale graphs. In: ICDE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yoon, M. (2021). Graph Fraud Detection Based on Accessibility Score Distributions. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12976. Springer, Cham. https://doi.org/10.1007/978-3-030-86520-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-86520-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86519-1
Online ISBN: 978-3-030-86520-7
eBook Packages: Computer ScienceComputer Science (R0)
