89 citations · 276 across the 19 of their papers we have counts for
19 papers
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…
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Han Huang, Leilei Sun, Bowen Du +1
Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress…
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
Le Yu, Leilei Sun, Bowen Du +1
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop int…
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
Mingzhe Liu, Han Huang, Hao Feng +3
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in…
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Han Huang, Leilei Sun, Bowen Du +1
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, acc…
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…
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…
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…
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.…
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning
Dongjie Wang, Kunpeng Liu, Pauline Johnson +3
Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage d…
Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data
Xuyan Tan, Yuhang Wang, Bowen Du +4
Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this lin…
Heterogeneous Graph Representation Learning with Relation Awareness
Le Yu, Leilei Sun, Bowen Du +3
Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link predicti…
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Le Yu, Leilei Sun, Bowen Du +3
Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to o…
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Junchen Ye, Leilei Sun, Bowen Du +2
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station…
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
Dongjie Wang, Pengyang Wang, Jingbo Zhou +3
While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can…
Predicting Temporal Sets with Deep Neural Networks
Le Yu, Leilei Sun, Bowen Du +3
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In pr…
Attentive Crowd Flow Machines
Lingbo Liu, Ruimao Zhang, Jiefeng Peng +3
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow ch…