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Found 2 papers, 1 papers with code
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Graph Anomaly Detection
Semi-supervised Anomaly Detection
+1
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems.
