activity
20182023
most citedHeterogeneous Graph Representation Learning with Relation Awareness

89 citations · 276 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.LG2022★ 2 cited

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…

cs.AI2022

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…

cs.LG2022★ 41 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…

cs.LG2022★ 1 cited

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…

cs.LG2022★ 2 cited

Label-Enhanced Graph Neural Network for Semi-supervised Node Classification

Le Yu, Leilei Sun, Bowen Du +2

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuab…

cs.LG2022★ 2 cited

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.…