Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

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 …

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 …

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield
drastically improved performance compared to unsupervised models. However, they still …

Anomaly detection in graphs has attracted considerable interests in both academia and
industry due to its wide applications in numerous domains ranging from finance to biology …

Recent semi-supervised anomaly detection methods that are trained using small labeled
anomaly examples and large unlabeled data (mostly normal data) have shown largely …

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 anomaly detection in this paper aims to distinguish abnormal nodes that behave
differently from the benign ones accounting for the majority of graph-structured instances …

Anomaly detection (AD) is a crucial task in machine learning with various applications, such
as detecting emerging diseases, identifying financial frauds, and detecting fake news …