Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …

Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

In this paper, we address the issue of modeling and estimating changes in the state of the
spatio-temporal dynamical systems based on a sequence of observations like video frames …

Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based
learning tasks. Notably, most current GNN architectures operate under the assumption of …

Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …

Credit card fraud poses a significant threat to the economy. While Graph Neural Network
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …

Traffic accident prediction is an important research problem, which can help to identify
dangerous situations on the road in advance and take appropriate measures. Nonetheless …

Traffic forecasting is a challenging research topic due to the complex spatial and temporal
dependencies among different roads. Though great efforts have been made on traffic …

Generalizable and transferrable graph representation learning endows graph neural
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …

Due to the stochastic nature of events, predicting the duration of a traffic incident presents a
formidable challenge. Accurate duration estimation can result in substantial advantages for …