Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …

Spatiotemporal (ST) learning has become a crucial technique for urban digitalization. Due to
expansions and dynamics of cities, current spatiotemporal models are inclined to suffer …

Subgraph learning has received considerable attention in its capacity of interpreting
important structural information for predictions. Existing subgraph learning usually exploits …

Pre-training GNNs to extract transferable knowledge and apply it to downstream tasks has
become the de facto standard of graph representation learning. Recent works focused on …

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining.
Typical spatiotemporal data prediction methods usually capture spatial dependencies by …

Fusing multimodal heterogeneous data plays a vital role in recognition and prediction tasks
in various fields, eg, action recognition and traffic accident forecast. Yet, there remain some …

Time series forecasting (TSF) plays a pivotal role in many real-world applications. Recently,
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …

Advanced metering infrastructure (AMI) has been widely used as an intelligent energy
consumption measurement system. Electric power was the representative energy source …

Graph Neural Networks (GNNs) can learn representative graph-level features to achieve
efficient graph classification. But GNNs usually assume an environment where both class …

The steel industry has received serious attention under the background of carbon
neutralization and carbon peaking. However, the traditional end-to-end energy consumption …