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 …

Social network analysis is an important problem in data mining. A fundamental step for
analyzing social networks is to encode network data into low-dimensional representations …

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) suffers from heterophily—abnormal nodes are sparse so
that they are connected to vast normal nodes. The current solutions upon Graph Neural …

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

Network representation learning (RL) aims to transform the nodes in a network into
lowdimensional vector spaces while preserving the inherent properties of the network …

As one of the major frauds in financial services, cash-out fraud is that users pursue cash
gains with illegal or insincere means. Conventional solutions for the cash-out user detection …

Attributed network embedding is the task to learn a lower dimensional vector representation
of the nodes of an attributed network, which can be used further for downstream network …

Abstract Graph Neural Networks (GNNs) are powerful tools for graph representation
learning. Despite their rapid development, GNNs also face some challenges, such as over …

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 …