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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“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]
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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