Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Understand

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.

• Previous network embedding based methods have been mostly focusing on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in dynamic graphs.
• In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs.
• In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph.