AI 中文总结
本文借鉴VCG原理提出基于排除的异常检测方法，结合嵌入框架分离潜在合谋社区，经ICLR 2021数据集验证，可高灵敏度检测同行评审合谋，为会议组织者提供可靠检测工具。
AI 中文摘要
同行评审是科学进步的基石，却日益受到复杂合谋团伙的破坏，这些团伙会系统性操纵评审结果以偏袒圈内成员。现有检测方法难以梳理显式合著图中模糊的社会关系，本文提出新方向：基于排除的异常检测。与VCG拍卖的运作方式类似，我们正式衡量可疑评审团的边际影响力，即便显式社交图隐藏，也能暴露其特征。为在无合谋团先验知识的情况下大规模应用该方法，我们引入基于嵌入的发现框架，该框架利用连续语义嵌入直接从语义特征中分离潜在合谋社区，绕过显式网络分析的对抗性局限。与传统基于启发式的方法不同，该框架作为自动审计器，无需团成员的先验知识，通过在独立诊断算法间执行解耦搜索并将结果组合成不同共识形态，使组织者可动态平衡检测的精确率与召回率。基于ICLR 2021的大规模数据集对该技术的评估显示，我们的方法能以高灵敏度和严格的家族式误差率（FWER）控制识别显性及微妙的对抗策略，为会议组织者提供了可扩展、鲁棒且隐私保护的工具，以保障学术出版的科学完整性。
英文摘要
The peer-review process, the bedrock of scientific advancement, is increasingly undermined by sophisticated collusion rings that systematically manipulate review outcomes to favor in-group members. While existing detection methods struggle to untangle obfuscated social ties in explicit co-authorship graphs, we introduce a new direction: Exclusion Based Anomaly Detection. Similar to the way VCG auctions work, we formally measure the marginal influence of suspected reviewer groups, exposing their signature even when explicit social graphs are hidden. To apply this at scale without prior knowledge of colluding groups, we introduce the Embedding Based Discovery Framework, which leverages continuous semantic embeddings to isolate latent collusive communities directly from their semantic profile, bypassing the adversarial limitations of explicit network analysis. Unlike traditional heuristic-based approaches, our framework functions as an automated auditor, requiring no prior knowledge of group membership. It achieves this by executing a decoupled search across independent diagnostic algorithms and combining their findings into distinct consensus formations, allowing organizers to dynamically balance detection precision and recall. Evaluating our technique with large-scale datasets (based on ICLR 2021) shows our method's capacity to identify both overt and subtle adversarial tactics with high sensitivity and strict Family-Wise Error Rate (FWER) control, effectively providing conference organizers with a scalable, robust, and privacy-preserving tool to secure the scientific integrity of academic publishing.
Comments39 pages, 9 figures
