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 …

Recent studies have demonstrated that machine learning approaches like deep learning
methods are easily fooled by adversarial attacks. Recently, a highly-influential study …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Given a large-scale rhythmic time series containing mostly normal data segments (or
'beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For …

Given a bipartite graph of users and the products that they review, or followers and
followees, how can we detect fake reviews or follows? Existing fraud detection methods …

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

Time series data naturally exist in many domains including medical data analysis,
infrastructure sensor monitoring, and motion tracking. However, a very small portion of …

How can we describe a large, dynamic graph over time? Is it random? If not, what are the
most apparent deviations from randomness--a dense block of actors that persists over time …

Fake news may be intentionally created to promote economic, political and social interests,
and can lead to negative impacts on humans beliefs and decisions. Hence, detection of fake …

Detecting anomalies on graph data has two types of methods. One is pattern mining that
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …