conformal detector
Anomalous Edge Detection in Edge Exchangeable Social Network Models
Luo, Rui, Nettasinghe, Buddhika, Krishnamurthy, Vikram
This paper studies detecting anomalous edges in directed graphs that model social networks. We exploit edge exchangeability as a criterion for distinguishing anomalous edges from normal edges. Then we present an anomaly detector based on conformal prediction theory; this detector has a guaranteed upper bound for false positive rate. In numerical experiments, we show that the proposed algorithm achieves superior performance to baseline methods.