With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes,
edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its …

Deep learning (DL) techniques have recently found success in anomaly detection (AD)
across various fields such as finance, medical services, and cloud computing. However …

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

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 …

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 …

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 …

Anomalous node detection in a static graph faces significant challenges due to the rarity of
anomalies and the substantial cost of labeling their deviant structure and attribute patterns …

Existing works on anomaly detection (AD) rely on clean labels from human annotators that
are expensive to acquire in practice. In this work, we propose a method to leverage …