94 citations · 159 across the 8 of their papers we have counts for
8 papers
DyGKT: Dynamic Graph Learning for Knowledge Tracing
Ke Cheng, Linzhi Peng, Pengyang Wang +3
Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length le…
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Yicheng Zhou, Pengfei Wang, Hao Dong +4
Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networ…
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Kun Yi, Qi Zhang, Wei Fan +6
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually…
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Kun Yi, Qi Zhang, Wei Fan +7
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many s…
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
Xuanming Hu, Wei Fan, Dongjie Wang +3
Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern e…
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Hao Dong, Zhiyuan Ning, Pengyang Wang +4
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of work…