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 detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

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 …

Rating platforms enable large-scale collection of user opinion about items (eg, products or
other users). However, untrustworthy users give fraudulent ratings for excessive monetary …

The objective of fraud detection is to distinguish fraudsters from normal users. In
graph/network environments, both fraudsters and normal users are modeled as nodes, and …

Anomaly detection in dynamic graphs becomes very critical in many different application
scenarios, eg, recommender systems, while it also raises huge challenges due to the high …

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

GNN-based anomaly detection has recently attracted considerable attention. Existing
attempts have thus far focused on jointly learning the node representations and the classifier …

The huge volume of emerging graph datasets has become a double-bladed sword for graph
machine learning. On the one hand, it empowers the success of a myriad of graph neural …