most citedDeciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

15 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA20243 cited

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

Guibin Zhang, Yanwei Yue, Zhixun Li +6

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

cs.LG2024

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning

Kang Luo, Yuanshao Zhu, Wei Chen +4

Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns. Nevertheless, existing studies typically ignore…

cs.LG2024

Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts

Kun Wang, Hao Wu, Guibin Zhang +5

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.…

cs.LG20241 cited

Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness

Guibin Zhang, Yanwei Yue, Kun Wang +7

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-essen…

cs.LG202315 cited

Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Yutong Xia, Yuxuan Liang, Haomin Wen +4

Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG…