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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
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
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
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
Eswaran, D., Faloutsos, C., Guha, S., & Mishra, N. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220040 · Cited by 145
Self-citationFRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance
Zhang, G., Wu, J. X., Yang, J. Y., Beheshti, A., Xue, S., Zhou, C. D., & Sheng, Q. Z. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00098 · Cited by 144
Gao, Y., Li, X., Peng, H. M., Fang, B., & Yu, P. S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2987019 · Cited by 127
Decoupling Representation Learning and Classification for GNN-based Anomaly Detection
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., & Chen, H. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462944 · Cited by 131
CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms
Shin, K., Eliassi‐Rad, T., & Faloutsos, C. (2016) · Proceedings · doi:10.1109/icdm.2016.0058 · Cited by 111
Self-citationGAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Roy, A., Shu, J., Li, J., Yang, C., Elshocht, O., Smeets, J., & Li, P. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635767 · Cited by 122
Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection
Liu, C., Sun, L., Ao, X., Feng, J., He, Q., & Yang, H. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467142 · Cited by 109
Wang, B., Gong, N. Z., & Fu, H. (2017) · Proceedings · doi:10.1109/icdm.2017.56 · Cited by 92
Deep Structure Learning for Fraud Detection
Wang, H., Zhou, C. D., Wu, J. X., Dang, W., Xingquan Zhu, A., & Wang, J. (2018) · Proceedings · doi:10.1109/icdm.2018.00072 · Cited by 86
M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees
Shin, K., Hooi, B., & Faloutsos, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46128-1_17 · Cited by 82
Self-citationLiu, S., Hooi, B., & Faloutsos, C. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133018 · Cited by 75
Self-citationAntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks
Chen, T., & Tsourakakis, C. E. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539100 · Cited by 78
GCCAD: Graph Contrastive Learning for Anomaly Detection
Chen, B., Zhang, J., Zhang, X., Dong, Y., Song, J., Zhang, P., Xu, K., Kharlamov, E., & Tang, J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3200459 · Cited by 76
Efficient Algorithms for Densest Subgraph Discovery on Large Directed Graphs
Ma, C., Fang, Y., Cheng, R. C. K., Lakshmanan, L. V. S., Zhang, W., & Lin, X. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389697 · Cited by 74
Fishing for Fraudsters: Uncovering Ethereum Phishing Gangs With Blockchain Data
Liu, J., Chen, J., Wu, J., Wu, Z., Fang, J., & Zheng, Z. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3359000 · Cited by 70
Patterns and anomalies in k-cores of real-world graphs with applications
Shin, K., Eliassi‐Rad, T., & Faloutsos, C. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1077-6 · Cited by 75
Self-citationError-Bounded Graph Anomaly Loss for GNNs
Zhao, T., Deng, C., Yu, K., Jiang, T., Wang, D., & Jiang, M. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411979 · Cited by 69
Robust Spammer Detection by Nash Reinforcement Learning
Dou, Y., Ma, G., Yu, P. S., & Xie, S. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403135 · Cited by 65
Shin, K., Hooi, B., Kim, J., & Faloutsos, C. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098087 · Cited by 65
Self-citationLOBO: Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Echeverra, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Stringhini, G., & Zhou, S. (2018) · Proceedings of the 34th Annual Computer Security Applications Conference · doi:10.1145/3274694.3274738 · Cited by 61
“Other approaches leverage bots’ similarity in their operation, such as synchronization in posting messages [8], accesses by a common set of IP addresses [23, 32], or similar uses of the accounts [39].”
Anomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications
Dumitrescu, B., Bălțoiu, A., & Budulan, Ş. (2022) · IEEE Access · doi:10.1109/access.2022.3170467 · Cited by 75
Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach
Kaghazgaran, P., Caverlee, J., & Squicciarini, A. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159726 · Cited by 59
Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook
Kurshan, E., Shen, H., & Yu, H. (2020) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai49837.2020.00029 · Cited by 56
Shin, K., Hooi, B., Kim, J., & Faloutsos, C. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018676 · Cited by 58
Self-citationMining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks
Yang, Y., Xu, Y., Sun, Y., Dong, Y., Wu, F. Q., & Zhuang, Y. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2924431 · Cited by 56
RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection
Guo, D., Liu, Z., & Li, R. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.026 · Cited by 53
SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields
El-Mawass, N., Honeiné, P., & Vercouter, L. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102317 · Cited by 54
Group-based Fraud Detection Network on e-Commerce Platforms
Yu, J., Wang, H., Wang, X., Li, Z., Qin, L., Zhang, W., Liao, J., & Zhang, Y. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599836 · Cited by 53
Cost-Sensitive GNN-Based Imbalanced Learning for Mobile Social Network Fraud Detection
Hu, X., Chen, H.-T., Chen, H.-C., Liu, S.-X., Li, X., Zhang, S.-B., Wang, Y.-H., & Xue, X.-Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3302651 · Cited by 63
A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning
Zhao, T., Jiang, T., Shah, N., & Jiang, M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3102609 · Cited by 51
Self-citationTrustworthiness Assessment of Users in Social Reviewing Systems
Esposito, C., Moscato, V., & Sperlí, G. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3049082 · Cited by 46
(2020) · doi:10.14778/3494124.3494128 · Cited by 46
Jiang, N., Duan, F., Chen, H., Huang, W., & Liu, X. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2021.3132672 · Cited by 46
FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams
Khodabandehlou, S., & Golpayegani, A. H. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3641857 · Cited by 45
A Survey on the Densest Subgraph Problem and its Variants
Lanciano, T., Miyauchi, A., Fazzone, A., & Bonchi, F. (2024) · ACM Computing Surveys · doi:10.1145/3653298 · Cited by 45
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Ren, Y., Zhu, H., Zhang, J., Dai, P., & Bo, L. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00197 · Cited by 45
Kernel Ridge Regression-Based Graph Dataset Distillation
Xu, Z., Chen, Y., Pan, M., Chen, H., Das, M., Yang, H., & Tong, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599398 · Cited by 41
Generic anomalous vertices detection utilizing a link prediction algorithm
Kagan, D., Elovichi, Y., & Fire, M. (2018) · Social Network Analysis and Mining · doi:10.1007/s13278-018-0503-4 · Cited by 43
(p,q)-biclique counting and enumeration for large sparse bipartite graphs
Yang, J., Peng, Y. F., & Zhang, W. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3489496.3489497 · Cited by 40
Fake Online Reviews: A Unified Detection Model Using Deception Theories
Abdulqader, M., Namoun, A., & Alsaawy, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3227631 · Cited by 49
ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks
Wen, R., Wang, J., Wu, C., & Xiong, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3391266 · Cited by 39
Fu, C., Liu, G., Yuan, K., & Wu, J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3523107 · Cited by 43
A Contrast Metric for Fraud Detection in Rich Graphs
Liu, S., Hooi, B., & Faloutsos, C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876531 · Cited by 36
Self-citationScalable Semi-Supervised Graph Learning Techniques for Anti Money Laundering
Karim, M. R., Hermsen, F., Chala, S. A., de Perthuis, P., & Mandal, A. (2024) · IEEE Access · doi:10.1109/access.2024.3383784 · Cited by 54
FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection
Lin, J., Guo, X., Zhu, Y., Mitchell, S., Altman, E., & Shun, J. (2024) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3677052.3698648 · Cited by 36
DenseFlow: Spotting Cryptocurrency Money Laundering in Ethereum Transaction Graphs
Lin, D., Wu, J., Yu, Y., Fu, Q., Zheng, Z., & Yang, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645692 · Cited by 35
Reliable fake review detection via modeling temporal and behavioral patterns
Wu, X., Dong, Y., Tao, J., Huang, C., & Chawla, N. V. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8257963 · Cited by 34
FraudNE: a Joint Embedding Approach for Fraud Detection
Zheng, M., Zhou, C. D., Wu, J. X., Pan, S., Shi, J., & Guo, L. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489585 · Cited by 34
SC-Com: Spotting Collusive Community in Opinion Spam Detection
Byun, H., Jeong, S., & Kim, C.-K. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102593 · Cited by 34
On Directed Densest Subgraph Discovery
Ma, C., Fang, Y., Cheng, R. C. K., Lakshmanan, L. V. S., Zhang, W., & Lin, X. (2021) · ACM Transactions on Database Systems · doi:10.1145/3483940 · Cited by 37
Graph Mining for Cybersecurity: A Survey
Yan, B., Yang, C. H., Shi, C., Fang, Y., Li, Q., Ye, Y., & Du, J. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3610228 · Cited by 44
Temporal burstiness and collaborative camouflage aware fraud detection
Zhang, Z., Wan, J., Zhou, M., Lai, Z., Tessone, C. J., Chen, G., & Liao, H. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103170 · Cited by 33
Antisocial Behavior on the Web
Kumar, S., Cheng, J., & Leskovec, J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3051106 · Cited by 36
GADM: Manual fake review detection for O2O commercial platforms
Ruan, N., Deng, R., & Su, C. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101657 · Cited by 32
Efficient Algorithms for Maximal k-Biplex Enumeration
Yu, K., Long, C., Liu, S., & Yan, D. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3517847 · Cited by 32
Cao, B., Mao, M., Viidu, S., & Yu, P. S. (2017) · Proceedings · doi:10.1109/icdm.2017.90 · Cited by 30
A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery
Ma, C., Fang, Y., Cheng, R. C. K., Lakshmanan, L. V. S., & Han, X. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3517837 · Cited by 31
Jiang, J., Li, Y., He, B., Hooi, B., Chen, J., & Kang, J. K. Z. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3570690.3570696 · Cited by 30
Self-citationCollusion-aware detection of review spammers in location based social networks
Cao, J., Xia, R., Guo, Y., & Ma, Z. (2018) · World Wide Web · doi:10.1007/s11280-018-0614-x · Cited by 30
Wang, L., Li, P., Xiong, K., Zhao, J.-S., & Lin, R. (2021) · International Conference on Information and Knowledge Management · doi:10.1145/3459637.3482277 · Cited by 36
Telecom Fraud Detection via Hawkes-enhanced Sequence Model
Jiang, Y. H., Liu, G., Wu, J., & Lin, H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3150803 · Cited by 28
Graph Learning for Fake Review Detection
Yu, S., Ren, J. F., Li, S., Naseriparsa, M., & Xia, F. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.922589 · Cited by 30
(p,q)-biclique counting and enumeration for large sparse bipartite graphs
Yang, J., Peng, Y. F., Ouyang, D., Zhang, W., Lin, X., & Zhao, X. H. (2023) · The VLDB Journal · doi:10.1007/s00778-023-00786-0 · Cited by 26
No Place to Hide: Catching Fraudulent Entities in Tensors
Ban, Y., Xin, L., Ling, H., Duan, Y., Liu, X., & Xu, W. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313403 · Cited by 25
A subgraph-based knowledge reasoning method for collective fraud detection in E-commerce
Song, J., Qu, X., Hu, Z., Li, Z., Gao, J., & Zhang, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.134 · Cited by 28
The Art and Craft of Fraudulent App Promotion in Google Play
Rahman, M., Hernandez, N., Recabarren, R., Ahmed, S. I., & Carbunar, B. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3345658 · Cited by 25
SliceNDice: Mining Suspicious Multi-Attribute Entity Groups with Multi-View Graphs
Nilforoshan, H., & Shah, N. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00050 · Cited by 24
Self-citationAdaRisk: Risk-Adaptive Deep Reinforcement Learning for Vulnerable Nodes Detection
Li, F., Xu, Z., Cheng, D., & Wang, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3409869 · Cited by 23
Maximum k-Biplex Search on Bipartite Graphs: A Symmetric-BK Branching Approach
Yu, K., & Long, C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588729 · Cited by 23
SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks
Futia, G., Vetrò, A., & Martin, J. C. D. (2020) · SoftwareX · doi:10.1016/j.softx.2020.100516 · Cited by 25
Opinion Spam Detection Based on Heterogeneous Information Network
Sun, Y., & Loparo, K. A. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00277 · Cited by 21
An anomaly detection framework for time-evolving attributed networks
Xue, L., Chen, Y., Luo, M., Peng, Z., & Liu, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.047 · Cited by 22
Scaling Up k-Clique Densest Subgraph Detection
He, Y., Wang, K., Zhang, W., Lin, X., & Zhang, Y. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588923 · Cited by 22
Querying in the Age of Graph Databases and Knowledge Graphs
Arenas, M., Gutiérrez, C., & Sequeda, J. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457545 · Cited by 21
Adaptive Label Propagation for Group Anomaly Detection in Large-Scale Networks
Li, Z., Chen, X., Song, J., & Gao, J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3176478 · Cited by 22
Finding locally densest subgraphs
Ma, C., Cheng, R. C. K., Lakshmanan, L. V. S., & Han, X. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3551793.3551826 · Cited by 21
Anonymous Edge Representation for Inductive Anomaly Detection in Dynamic Bipartite Graph
Fang, L., Feng, K., Gui, J., Feng, S., & Hu, A. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3579075.3579088 · Cited by 21
Measuring and mitigating oauth access token abuse by collusion networks
Farooqi, S., Zaffar, F., Leontiadis, N., & Shafiq, Z. (2017) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3131365.3131404 · Cited by 22
Detecting problematic transactions in a consumer-to-consumer e-commerce network
Kodate, S., Chiba, R., Kimura, S., & Masuda, N. (2020) · Applied Network Science · doi:10.1007/s41109-020-00330-x · Cited by 24
Efficient critical relationships identification in bipartite networks
Chen, C., Zhu, Q., Wu, Y., Sun, R., Wang, X., & Liu, X. (2021) · World Wide Web · doi:10.1007/s11280-021-00914-2 · Cited by 20
Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
Dimitriadis, I., Georgiou, K., & Vakali, A. I. (2021) · Applied Sciences · doi:10.3390/app11219857 · Cited by 22
CubeFlow: Money Laundering Detection with Coupled Tensors
Sun, X., Zhang, J., Zhao, Q., Liu, S., Chen, J., Zhuang, R., Shen, H., & Cheng, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_7 · Cited by 20
Efficient and Effective Algorithms for Generalized Densest Subgraph Discovery
Xu, Y., Ma, C., Fang, Y., & Bao, Z. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3589314 · Cited by 19
Graph Anomaly Detection Based on Steiner Connectivity and Density
Cadena, J., Chen, F., & Vullikanti, A. K. S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2813311 · Cited by 21
Modeling User Behavior With Interaction Networks for Spam Detection
Agarwal, P., Srivastava, M., Singh, V., & Rosenberg, C. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531875 · Cited by 19
Identifying similar-bicliques in bipartite graphs
Yao, K. L., Chang, L., & Yu, J. X. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3551793.3551854 · Cited by 17
Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic Graphs
Jiang, J., Chen, Y., He, B., Chen, M., & Chen, J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3394155 · Cited by 17
Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem
Li, Q., Wu, Q., Zhu, C., Zhang, J., & Zhao, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16148-4_18 · Cited by 17
Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion
Esposito, C., Galli, A., Moscato, V., & Sperlí, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.012 · Cited by 18
Fraud De-Anonymization for Fun and Profit
Hernandez, N., Rahman, M., Recabarren, R., & Carbunar, B. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243770 · Cited by 16
Correlated Anomaly Detection from Large Streaming Data
Chen, Z., Yu, X., Yuan, L., Song, B., Quan, W., Hu, X., & Yan, E. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622004 · Cited by 16
DFraud³: Multi-Component Fraud Detection Free of Cold-Start
Shehnepoor, S., Togneri, R. B., Liu, W., & Bennamoun, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3081258 · Cited by 16
Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & OutlookPreprint
Kurshan, E., Shen, H., & Yu, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.01854 · Cited by 16
GCCAD: Graph Contrastive Coding for Anomaly DetectionPreprint
Chen, B., Zhang, J., Zhang, X., Dong, Y., Song, J., Zhang, P., Xu, K., Kharlamov, E., & Tang, J. (2021) · arXiv (Cornell University) · Cited by 16
Large-scale Fake Click Detection for E-commerce Recommendation Systems
Li, J., Li, Z., Huang, J., Zhang, J., Wang, X., Lu, X., & Zhou, J. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00290 · Cited by 15
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 ›
Are you an author, or do you see a problem with this page? Request a review
