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.
@article{arxiv.2109.12727,
title = {Anomalous Edge Detection in Edge Exchangeable Social Network Models},
author = {Rui Luo and Buddhika Nettasinghe and Vikram Krishnamurthy},
journal= {arXiv preprint arXiv:2109.12727},
year = {2023}
}