  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.
