89 citations · 248 across the 22 of their papers we have counts for
6 papers · 1 filter
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
Han Huang, Leilei Sun, Bowen Du +2
Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
Dongjie Wang, Lingfei Wu, Denghui Zhang +3
The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…
Automated Urban Planning aware Spatial Hierarchies and Human Instructions
Dongjie Wang, Kunpeng Liu, Yanyong Huang +3
Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginat…
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…
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…
Continuous-Time User Preference Modelling for Temporal Sets Prediction
Le Yu, Zihang Liu, Leilei Sun +3
Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set.…