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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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
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 9,032
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,307
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
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
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
Combating Web Spam with TrustRank
Gyöngyi, Z., García-Molina, H., & Pedersen, J. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50052-8 · Cited by 1,087
Yu, H., Kaminsky, M., Gibbons, P. B., & Flaxman, A. D. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1151659.1159945 · Cited by 734
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
Yu, H., Gibbons, P. B., Kaminsky, M., & Xiao, F. (2008) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2008.13 · Cited by 570
Greedy Approximation Algorithms for Finding Dense Components in a Graph
Charikar, M. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44436-x_10 · Cited by 493
Flagged first, then by citations
1 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- 10.5555/2228298.2228319 · DOI
- 10.5555/646688.702972 · DOI
- 10.5555/1316689.1316740 · DOI
- G. Karypis and V. Kumar. METIS: Unstructured graph partitioning and sparse matrix ordering system. The University of Minnesota, 2, 1995.
- 10.5555/2002472.2002512 · DOI
- 10.5555/2124405 · DOI
- 10.5555/1558977.1558979 · DOI
Cited by
Sorted by citations
Items 301–305 of 305
Polarity-Driven Ensemble Framework for Online Fake Reviews Detection
Abdullshaheed, Z. Z., & Obaid, A. J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23314-1_11
Generate and Filter: A GNN-Based Approach for Graph Anomaly Detection
Li, M., Liu, Y., Li, X., Huang, L., Giunchiglia, F., Liang, Y., Feng, X., & Guan, R. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3724665
Maximum Defective Biclique Search in Large Bipartite Graphs
Cui, D., Li, R.-H., Dai, Q., Qin, H., & Wang, G. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3836663.3836687
Contradiction-Aware Dual-View Graph Learning for Wikipedia Sockpuppet Detection
Pirrò, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37657-2_32
Higher-Order and Dual-Geometry Modeling for Blockchain Anti-Money Laundering Detection
Zhang, Q., Jiang, M., Shui, Y., Li, Z., Zhu, H., & Chai, H. (2026) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws72778.2026.00030
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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