On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
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Abstract (arXiv)
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipulate the conference's paper assignment, with the aim of being assigned to review each other's papers. The most straightforward way that colluding reviewers can manipulate the paper assignment is by indicating their interest in each other's papers through strategic paper bidding. One potential approach to solve this important problem would be to detect the colluding reviewers from their manipulated bids, after which the conference can take appropriate action. While prior work has developed effective techniques to detect other kinds of fraud, no research has yet established that detecting collusion rings is even possible. In this work, we tackle the question of whether it is feasible to detect collusion rings from the paper bidding. To answer this question, we conduct empirical analysis of two realistic conference bidding datasets, including evaluations of existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other's papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores).
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 43 of its 155 references that are in the map. First posted on arXiv 12 Feb 2024.
Builds on
43 of 155 references.
References · 43
- 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
- 2014Graph-based Anomaly Detection and Description: A Surveycited by 183
- 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 155
- 2018Construction of the Literature Graph in Semantic Scholarcited by 118
- 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
- 2020Mitigating Manipulation in Peer Review via Randomized Reviewer Assignmentscited by 38
- 2016Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphscited by 37
- 2016Scalable Anomaly Ranking of Attributed Neighborhoodscited by 33
- 2017Design and Analysis of the NIPS 2016 Review Processcited by 33
- 2019Statistical Analysis of Nearest Neighbor Methods for Anomaly Detectioncited by 29
- 2022Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solutioncited by 24
- 2022Toward Unsupervised Outlier Model Selectioncited by 19
31 more
- 2022Matching Papers and Reviewers at Large Conferencescited by 18
- 2020AutoAudit: Mining Accounting and Time-Evolving Graphscited by 15
- 2020Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessmentcited by 14
- 2017Explaining Anomalies in Groups with Characterizing Subspace Rulescited by 13
- 2019A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classificationcited by 13
- 2021Making Paper Reviewing Robust to Bid Manipulation Attackscited by 12
- 2022Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Successcited by 11
- 2023A Gold Standard Dataset for the Reviewer Assignment Problemcited by 11
- 2024Unified Discrete Diffusion for Categorical Datacited by 11
- 2015EdgeCentric: Anomaly Detection in Edge-Attributed Networkscited by 9
- 2020Automating Outlier Detection via Meta-Learningcited by 9
- 2022A Dataset on Malicious Paper Bidding in Peer Reviewcited by 9
- 2022Graph Anomaly Detection with Unsupervised GNNscited by 8
- 2023No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learningcited by 7
- 2021Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Seriescited by 6
- 2021Combating Collusion Rings is Hard but Possiblecited by 6
- 2023DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selectioncited by 6
- 2023Fast Unsupervised Deep Outlier Model Selection with Hypernetworkscited by 6
- 2015BIRDNEST: Bayesian Inference for Ratings-Fraud Detectioncited by 5
- 2020Anomaly Detection in Large Labeled Multi-Graph Databasescited by 5
- 2024Descriptive Kernel Convolution Network with Improved Random Walk Kernelcited by 5
- 2021Fast Attributed Graph Embedding via Density of Statescited by 4
- 2022Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignmentcited by 4
- 2023Self-Tuning Self-Supervised Image Anomaly Detectioncited by 4
- 2015Less is More: Building Selective Anomaly Ensemblescited by 3
- 2020Connecting Graph Convolutional Networks and Graph-Regularized PCAcited by 3
- 2018Incorporating Privileged Information to Unsupervised Anomaly Detectioncited by 2
- 2022Summarizing Labeled Multi-Graphscited by 2
- 2015Where Graph Topology Matters: The Robust Subgraph Problemcited by 1
- 2016The Politics of Routing: Investigating the Relationship Between AS Connectivity and Internet Freedomcited by 1
- 2017Ties That Bind - Characterizing Classes by Attributes and Social Tiescited by 1
Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 2.
7 papers.
- 2024OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviewscited by 65
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewardscited by 34
- 2024What Can Natural Language Processing Do for Peer Review?cited by 25
- 2025Insights from the ICLR Peer Review and Rebuttal Processcited by 11
- 2024Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusionscited by 7
- 2026CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Reviewcited by 1
- 2026Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit Systemcited by 0
- 2026CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review4 Sep 2026
- 2026Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System5 Jun 2026
- 2025Insights from the ICLR Peer Review and Rebuttal Process19 Nov 2025
- 2025Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards8 May 2025
- 2024OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews16 Dec 2024
- 2024Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions9 Dec 2024
- 2024What Can Natural Language Processing Do for Peer Review?10 May 2024