Cited by 305Spam and Phishing DetectionComplex Network Analysis TechniquesMisinformation and Its Impacts

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FRAUDAR: Bounding Graph Fraud in the Face of Camouflage

Hooi, Bryan, Song, Hyun Ah, Beutel, Alex, Shah, Neil, Shin, Kijung, Faloutsos, Christos·2016·Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

DOI 10.1145/2939672.2939747

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Abstract

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look "normal". Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage-resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower-followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4000 detected accounts, of which a majority had tweets showing that they used follower-buying services.

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Authors: Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C.
Affiliations: Carnegie Mellon University (US)
ORCID: Hooi, Bryan
Song, Hyun Ah
Beutel, Alex
Shah, Neil
Shin, Kijung
Faloutsos, Christos
Venue: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher: ACM
Published: 2016-08-13
Pages: 895-904
DOI: 10.1145/2939672.2939747
OpenAlex: W2348679751
Cited by: 305
References: 32
Citations by year: 2026: 26 · 2025: 27 · 2024: 36 · 2023: 35 · 2022: 34 · 2021: 51 · 2020: 28 · 2019: 23 · 2018: 27 · 2017: 16 · 2016: 2
FWCI: 19.25
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor, 365 d)
Locations: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
Funders: National Science Foundation (IIS-1408924, CNS-1314632, DGE-1252522)
Topics: Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords: fraud detection, fake reviews, fake followers, bipartite graphs, subgraph detection

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FRAUDAR: Bounding Graph Fraud in the Face of Camouflage

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