Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …

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 …

Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the
majority within a graph, has garnered significant attention. However, current GAD methods …

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

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 …

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 …

Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related
downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are …

PyGOD is an open-source Python library for detecting outliers in graph data. As the first
comprehensive library of its kind, PyGOD supports a wide array of leading graph-based …

Graph Neural Networks (GNNs) have shown remarkable performance in various tasks.
However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally …