Anomalous Edge Detection in Edge Exchangeable Social Network Models
This work addresses anomaly detection in social networks, offering a method with theoretical guarantees, but it appears incremental as it builds on existing edge exchangeability and conformal prediction frameworks.
The paper tackles the problem of detecting anomalous edges in directed social network graphs by leveraging edge exchangeability and conformal prediction, resulting in a detector with a guaranteed false positive rate bound and superior performance over baselines in experiments.
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.