most citedDecoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs

7 citations · 7 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

Jiawen Chen, Qi Shao, Yongjian Chang +3

Spatio-temporal traffic forecasting is a fundamental big data analytics task for intelligent transportation systems, where massive urban sensor streams exhibit heterogeneous, non-s…

cs.LG2026

Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks

Jiawen Chen, Qi Shao, Zhiqiang Ge +2

Topological neural networks have emerged as effective tools for modeling higher-order relational structures beyond pairwise graphs, including hypergraphs, simplicial complexes, and…

cs.LG2026

From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

Qi Shao, Hao Guo, Jiawen Chen +2

Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. However, these methods typically…

cs.LG2026

Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations

Qi Shao, Duxin Chen, Jiawen Chen +5

Predicting the behavior of ultra-large complex systems, from climate to biological and technological networks, is a central unsolved challenge. Existing approaches face a fundament…

cs.LG2026

CCMamba: Topologically-Informed Selective State-Space Networks on Combinatorial Complexes for Higher-Order Graph Learning

Jiawen Chen, Qi Shao, Mingtong Zhou +2

Topological deep learning has emerged as a powerful paradigm for modeling higher-order relational structures beyond pairwise interactions that standard graph neural networks fail t…

cs.AI2025

DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs

Jiawen Chen, Yanyan He, Qi Shao +4

Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing meth…

cs.LG2025★ 7 cited

Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs

Jiawen Chen, Qi Shao, Duxin Chen +1

Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often strugg…