Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Twitter bot detection has become an increasingly important task to combat misinformation,
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

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 …

As Graph Neural Networks (GNNs) become increasingly prevalent in a variety of fields, from
social network analysis to protein-protein interaction studies, growing concerns have …

Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within
graphs, finding applications in network security, fraud detection, social media spam …

Recently, graph-based anomaly detection (GAD) has attracted rising attention due to its
effectiveness in identifying anomalies in relational and structured data. Unfortunately, the …

Node outlier detection in attributed graphs is a challenging problem for which there is no
method that would work well across different datasets. Motivated by the state-of-the-art …

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 …