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 …

Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …

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 …

With the development of e-commerce, fraud behaviors have been becoming one of the
biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking …

Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes 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 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 …

Effectively detecting anomalous nodes in attributed networks is crucial for the success of
many real-world applications such as fraud and intrusion detection. Existing approaches …

Anomalies are ubiquitous in real-world time-series data which call for effective and timely
detection, especially in an unsupervised setting for labeling cost saving. In this paper, we …

Detecting anomalies on graph data has two types of methods. One is pattern mining that
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …