Ji He

RECAL is presented, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns and calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence, helping distinguish attacks from benign behavior and reduce false alarms.