172 citations · 247 across the 5 of their papers we have counts for
5 papers
Graph Neural Processes for Spatio-Temporal Extrapolation
Junfeng Hu, Yuxuan Liang, Zhencheng Fan +3
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data a…
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +4
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to est…
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recomme…
Time-Aware Neighbor Sampling for Temporal Graph Networks
Yiwei Wang, Yujun Cai, Yuxuan Liang +3
We present a new neighbor sampling method on temporal graphs. In a temporal graph, predicting different nodes' time-varying properties can require the receptive neighborhood of var…
Predicting Urban Water Quality with Ubiquitous Data
Ye Liu, Yuxuan Liang, Shuming Liu +2
Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urba…