Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …

Abstract Graph Anomaly Detection (GAD) has recently become a hot research spot due to its
practicability and theoretical value. Since GAD emphasizes the application and the rarity of …

Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Anomalous node detection in a static graph faces significant challenges due to the rarity of
anomalies and the substantial cost of labeling their deviant structure and attribute patterns …

A critical aspect of graph neural networks (GNNs) is to enhance the node representations by
aggregating node neighborhood information. However, when detecting anomalies, the …

We reveal a one-class homophily phenomenon, which is one prevalent property we find
empirically in real-world graph anomaly detection (GAD) datasets, ie, normal nodes tend to …

Internet of Things (IoT) systems typically generate large amounts of sensory signals that get
involved to represent the states of the systems. Most existing methods focus on learning the …

Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate
significantly from most benign entities within an ever-changing graph network. However …