Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Anomaly detection from graph data has drawn much attention due to its practical
significance in many critical applications including cybersecurity, finance, and social …

Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern
and feature information are different from most normal graphs in a graph set, which is rarely …

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset
from normal graphs. Anomalous graphs represent a very few but essential patterns in the …

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are
abnormal in their structure and/or the features of their nodes, as compared to other graphs …

Anomaly detection on graphs plays a significant role in various domains, including
cybersecurity, e-commerce, and financial fraud detection. However, existing methods on …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

Graph-level anomaly detection has become a critical topic in diverse areas, such as
financial fraud detection and detecting anomalous activities in social networks. While most …

GNN-based anomaly detection has recently attracted considerable attention. Existing
attempts have thus far focused on jointly learning the node representations and the classifier …

Graphs are used widely to model complex systems, and detecting anomalies in a graph is
an important task in the analysis of complex systems. Graph anomalies are patterns in a …