欺诈检测要控制误报和漏报，这篇论文的ProtoCP把共形预测用在了时序图上，四个基准上都比现有方法预测集更小，做风控的可以看看。
ProtoCP是一种用于时序交互图边级欺诈检测的共形预测框架，针对欺诈数据中良性邻居主导和类别极度不平衡的问题，通过聚焦欺诈相关子图上下文提升校准效率。该方法利用学习到的原型抑制良性噪声，并引入邻域相对评分与时间分数扩散机制，实现稳定的类别条件校准。在YelpChi、S-FFSD、FTFD和BankSim四个欺诈基准上，ProtoCP以更小的预测集达到目标覆盖率，优于现有最先进基线。代码已开源。
Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection
Conformal prediction (CP) provides distribution-free coverage guarantees and has emerged as a principled tool for uncertainty quantification. In edge-level fraud detection on temporal interaction graphs, where false positives and false negatives both carry substantial cost, such coverage guarantees are particularly appealing for risk-aware decision making. However, directly applying existing graph conformal predictors yields inefficient prediction sets due to two recurring properties of fraud data. Fraudulent interactions are often embedded in benign-dominated neighborhoods that dilute calibration signals, while extreme class imbalance leaves scarce labeled-fraud support in the calibration split and leads to overly conservative class-conditional thresholds. To address these issues, we propose ProtoCP, a conformal prediction framework for edge-level fraud detection on temporal graphs. ProtoCP improves calibration efficiency by focusing calibration on fraud-relevant subgraph context and producing more stable nonconformity scores under class imbalance and temporal drift. Specifically, it leverages learned prototypes to suppress benign-dominated noise in the calibration context and introduces a neighborhood-relative scoring mechanism with temporal score diffusion for stable class-conditional calibration. Experiments on four fraud benchmarks (YelpChi, S-FFSD, FTFD, and BankSim) show that ProtoCP achieves the target coverage with consistently smaller prediction sets than state-of-the-art baselines. Our codes are available at https://github.com/Picard1701ent/ProtoCP.git