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 …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

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 …

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 shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

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 …

Unsupervised graph anomaly detection is crucial for various practical applications as it aims
to identify anomalies in a graph that exhibit rare patterns deviating significantly from the …

Graph anomaly detection (GAD) has attracted increasing attention in machine learning and
data mining. Recent works have mainly focused on how to capture richer information to …

Network anomaly detection is a crucial task since a few anomalies can cause huge losses.
Semi-supervised anomaly detection methods can effectively leverage a small number of …

A key component of many graph neural networks (GNNs) is the pooling operation, which
seeks to reduce the size of a graph while preserving important structural information …