most citedContinuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

41 citations · 41 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2023

Pretraining Language Models with Text-Attributed Heterogeneous Graphs

Tao Zou, Le Yu, Yifei Huang +2

In many real-world scenarios (e.g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships…

cs.LG2023

Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing

Wangyang Ying, Dongjie Wang, Kunpeng Liu +2

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from…

cs.AI2023

Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction

Tao Zou, Le Yu, Leilei Sun +3

Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential p…

cs.SI2023

Continuous-Time Graph Learning for Cascade Popularity Prediction

Xiaodong Lu, Shuo Ji, Le Yu +3

Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing…

cs.LG202241 cited

Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

Liangzhe Han, Xiaojian Ma, Leilei Sun +4

Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand…