Deep Anomaly Detection on Attributed Networks

2 code implementations • 2019 SIAM International Conference on Data Mining (SDM) 2019 • Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu

In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.

Anomaly Detection Attribute