COMEDY: Continuous-Time Anomalous Edge Detection in Dynamic Networks

Jie Liu, Jiamou Liu, Kaiqi Zhao, Wu Chen

Abstract: Anomaly detection in dynamic networks is a critical task with broad applications in fields such as recommendation systems, social networks, and financial transaction networks. Most existing anomaly detection approaches rely on discrete-time models that approximate dynamic networks as a sequence of static snapshots. However, real-world data is typically represented as dynamic networks characterized by continuous edge streams. As a result, these methods often fail to capture fine-grained temporal dynamics, leading to significant information loss and suboptimal detection performance. Addressing this gap, this paper tackles the detection of anomalous edges in continuous-time dynamic networks, a crucial task for ensuring the security and integrity of networks in graph-based data analytics. We introduce COMEDY, a novel Continuous-time anOMalous Edge detection framework in DYnamic network. COMEDY innovates a Continuous Dynamic Graph Neural Network that integrates mechanisms for filtering outdated information, encodes node spatial-temporal properties, and refines negative sampling strategies, with the aim of improving the accuracy of anomalous edge detection. Notably, COMEDY is deliberately designed so that all necessary operations can respond to each new edge in the input stream in a constant time (w.r.t. the graph size). Experimental results on six real datasets demonstrate that COMEDY outperforms state-of-the-art anomaly detection methods, with a maximum gain of 8.20% in terms of AUC.

External IDs:dblp:journals/tnse/LiuLZC26