The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
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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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“this signature is unlikely to form naturally and highly correlated to fraudulent activities [30, 57, 73]”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [57]
In the citing paper
“Decreasing account reuse decreases profits, as reputable accounts are often preferred in search rank fraud jobs [22,68,87].”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [22]
Citations
Cites
Authoritative sources in a hyperlinked environment
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Hidden factors and hidden topics
McAuley, J., & Leskovec, J. (2013) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2507157.2507163 · Cited by 1,565
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Signed networks in social media
Leskovec, J., Huttenlocher, D. P., & Kleinberg, J. M. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753532 · Cited by 1,431
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SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
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Research works printed but not linked: 7
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Cited by
Sorted by citations
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
Ma, X., Wu, J. X., Xue, S., Yang, J. Y., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3118815 · Cited by 939
A comprehensive survey of anomaly detection techniques for high dimensional big data
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 488
Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., & Subrahmanian, V. S. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159729 · Cited by 381
Self-citationFdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System
Wang, J., Wen, R., Wu, C., Huang, Y., & Xiong, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3316586 · Cited by 239
Data Augmentation for Deep Graph Learning
Ding, K., Xu, Z., Tong, H., & Liu, H. (2022) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3575637.3575646 · Cited by 271
Metadata
- Authors
- Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2016-08-13
- Pages
- 895-904
- 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
- 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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