Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Machine Learning and Deep Learning methods are widely adopted across financial
domains to support trading activities, mobile banking, payments, and making customer credit …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

In many machine learning applications, data are coming with multiple graphs, which is
known as the multiple graph learning problem. The problem of multiple graph learning is to …

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 …

This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …