Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs

arXiv:2403.09039v1 Announce Type: new
Abstract: Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this problem typically employ an unsupervised learning framework, capturing normality patterns with exclusive normal data during training and identifying deviations as anomalies during testing. However, these methods face critical drawbacks: they either only depend on proxy tasks for general representation without directly pinpointing normal patterns, or they neglect to differentiate between …

abstract anomaly anomaly detection arxiv autoencoder challenge cs.ai cs.lg data detection dynamic evolution exclusive framework graph graphs memories normal normality patterns spatial temporal training type unsupervised unsupervised learning