3 citations · 4 across the 5 of their papers we have counts for
5 papers
Dynamic Graph Representation Learning for Passenger Behavior Prediction
Mingxuan Xie, Tao Zou, Junchen Ye +2
Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and time…
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Ke Cheng, Linzhi Peng, Junchen Ye +2
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repea…
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…
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
Tao Zou, Yuhao Mao, Junchen Ye +1
Dynamic graph learning equips the edges with time attributes and allows multiple links between two nodes, which is a crucial technology for understanding evolving data scenarios li…
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
Junchen Ye, Zihan Liu, Bowen Du +4
Recent studies have shown great promise in applying graph neural networks for multivariate time series forecasting, where the interactions of time series are described as a graph s…