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 …

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 …

Despite the remarkable success of graph neural networks (GNNs) for graph representation
learning, they are generally built on the (unreliable) iid assumption across training and …

Attributed networks are pervasive in different domains, ranging from social networks, gene
regulatory networks to financial transaction networks. This kind of rich network …

Attributed networks are a type of graph structured data used in many real-world scenarios.
Detecting anomalies on attributed networks has a wide spectrum of applications such as …

The key point of anomaly detection on attributed networks lies in the seamless integration of
network structure information and attribute information. A vast majority of existing works are …

Anomaly detection in attributed networks (instance-to-instance dependencies and
interactions are available) has various applications such as monitoring suspicious accounts …

Random walks are widely adopted in various network analysis tasks ranging from network
embedding to label propagation. It could capture and convert geometric structures into …

In this paper, we propose a novel framework, called Semi-supervised Embedding in
Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation …