Leiden-Based Model for Detecting Anomalous Groups in Event Propagation

Lingkai Li, Bo Li, Jingyuan Li, Renrui Duan, Haiyang Wang

Abstract: In social networks, some individuals or groups create numerous fake identities to increase their control or influence, which severely undermines the integrity and credibility of the net-work. Therefore, identifying these anomalous users is particularly crucial. Past research has mainly focused on supervised or semi-supervised node detection, analyzing the behavior of individual users. However, in real-world social platforms, anomalous users often operate in an organized manner. To address this, we explore the real-time behavioral patterns of anomalous groups from the perspective of event propagation. Then, by using the Leiden algorithm for group detection and combining permanence methods with anomaly detection, we have constructed a model named LAGEP (Leiden-Based Model for Detecting Anomalous Groups in Event Propagation) for identifying anomalous groups. Through the analysis of a large amount of data collected from real social platforms, we found that LAGEP improves the F1 score by 5.1% compared to the best baseline method.