89 citations · 248 across the 22 of their papers we have counts for
13 papers · 1 filter
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Ke Cheng, Linzhi Peng, Junchen Ye +2
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repea…
DyGKT: Dynamic Graph Learning for Knowledge Tracing
Ke Cheng, Linzhi Peng, Pengyang Wang +3
Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length le…
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…
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.…
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…
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…