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.

翻译：本文研究如何在模拟社交网络的定向图表中发现异常边缘。 我们利用边缘可交换性作为区分异常边缘与正常边缘的标准。 然后我们根据符合的预测理论提出异常探测器; 这个探测器有保证的假正率上限。 在数字实验中,我们证明提议的算法取得了优于基线方法的性能。