Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior
within graphs, benefiting various domains such as fraud detection and social network …

The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous
nodes in an input graph while ensuring fairness and avoiding biased predictions against …

The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous
nodes in an input graph while avoiding biased predictions against individuals from sensitive …

Graph data is commonly used to represent complex relationships between different
individuals, such as social networks, financial networks, and microservice networks. Graph …

Outlier detection is a pivotal technique within the realm of data mining, serving to pinpoint
aberrant values nestled within datasets. It has been widely employed across diverse …

Graph anomaly detection attracts considerable interest across a variety of application
domains, including fraud detection within social networks, identifying money laundering …

With the deeper research on attributed networks, graph anomaly detection is becoming an
increasingly important topic. It aims to identify patterns deviating from a majority of nodes …

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary
objective of identifying anomalous nodes that deviate significantly from the majority. This …