6 citations · 39 across the 34 of their papers we have counts for
7 papers · 1 filter
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Wei Chen, Huaiyu Wan, Yuting Wu +4
Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowab…
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
Shuhan Wu, Huaiyu Wan, Wei Chen +3
Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), uti…
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
Haomin Wen, Youfang Lin, Lixia Wu +10
Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emergin…
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Xiaowei Mao, Haomin Wen, Hengrui Zhang +5
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service route of a worker given his current task pool, has received rising attention in recent years…
Origin-Destination Travel Time Oracle for Map-based Services
Yan Lin, Huaiyu Wan, Jilin Hu +4
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from…
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
Lixia Wu, Haomin Wen, Haoyuan Hu +10
Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed t…
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Haomin Wen, Youfang Lin, Yutong Xia +4
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic un…