195 citations · 369 across the 15 of their papers we have counts for
5 papers · 1 filter
Disentangled Dynamic Graph Deep Generation
Wenbin Zhang, Liming Zhang, Dieter Pfoser +1
Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of prot…
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints
Liming Zhang, Liang Zhao, Dieter Pfoser
Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distribu…
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
Liming Zhang, Liang Zhao, Shan Qin +1
The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design. However, many real-wor…
Taking the pulse of COVID-19: A spatiotemporal perspective
Chaowei Yang, Dexuan Sha, Qian Liu +32
The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including…
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization
Liming Zhang, Andreas Züfle, Dieter Pfoser
Urban areas provide us with a treasure trove of available data capturing almost every aspect of a population's life. This work focuses on mobility data and how it will help improve…