Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Exgc: Bridging efficiency and explainability in graph condensation
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 …
However, the associated computational and storage overheads raise concerns. In sight of …
MultiGPrompt for multi-task pre-training and prompting on graphs
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …
representation learning. However, their efficacy within an end-to-end supervised framework …
Two heads are better than one: Boosting graph sparse training via semantic and topological awareness
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 …
challenges when applied to large-scale graphs. A promising solution is to remove non …
The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs
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 …
learning tasks. Notably, most current GNN architectures operate under the assumption of …
Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance
H Wu, F Xu, Y Duan, Z Niu, W Wang, G Lu… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems
Recent advancements in large language model (LLM)-powered agents have shown that
collective intelligence can significantly outperform individual capabilities, largely attributed to …
collective intelligence can significantly outperform individual capabilities, largely attributed to …
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
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 …
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
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 …
of earth system data for deep-learning-oriented earth science, sadly introduce a daunting …