most citedAttention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DS2024

A Simple, Nearly-Optimal Algorithm for Differentially Private All-Pairs Shortest Distances

Jesse Campbell, Chunjiang Zhu

The all-pairs shortest distances (APSD) with differential privacy (DP) problem takes as input an undirected, weighted graph and outputs a private estimate o…

cs.LG2024

Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction

Haiyang Liu, Chunjiang Zhu, Detian Zhang

Traffic flow prediction plays a crucial role in alleviating traffic congestion and enhancing transport efficiency. While combining graph convolution networks with recurrent neural…

cs.LG2023

Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction

Haiyang Liu, Chunjiang Zhu, Detian Zhang +1

Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems. The complex and dynamic spatial-temporal dependencies make the traffic flow pred…

cs.LG20233 cited

Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting

Haiyang Liu, Chunjiang Zhu, Detian Zhang +1

Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-tempora…

cs.DS2023

Communication-Efficient Distributed Graph Clustering and Sparsification under Duplication Models

Chun Jiang Zhu

In this paper, we consider the problem of clustering graph nodes and sparsifying graph edges over distributed graphs, when graph edges with possibly edge duplicates are observed at…