Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

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 …

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …

Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational
challenges when applied to large-scale graphs. A promising solution is to remove non …

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 …

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …

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

Recent advancements in large language model (LLM)-powered agents have shown that
collective intelligence can significantly outperform individual capabilities, largely attributed to …

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 …

The ever-increasing sensor service, though opening a precious path and providing a deluge
of earth system data for deep-learning-oriented earth science, sadly introduce a daunting …