Graph Convolution: A High-Order and Adaptive Approach

29 June 2017

Papers citing "Graph Convolution: A High-Order and Adaptive Approach"

5 / 5 papers shown

Title
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction Naghmeh Shafiee Roudbari Zachary Patterson Ursula Eicker Charalambos (Charis) Poullis GNN AI4TS 11 2 0 08 Sep 2022
On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks Rodrigo de Medrano J. Aznarte 26 15 0 15 Jul 2020
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting Yiwen Sun Yulu Wang Kun Fu Zheng Wang Changshui Zhang Jieping Ye AI4TS GNN 14 9 0 23 Apr 2020
Spatial Graph Convolutional Networks Tomasz Danel P. Spurek Jacek Tabor Marek Śmieja Lukasz Struski Agnieszka Słowik Lukasz Maziarka GNN 32 10 0 11 Sep 2019
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting Zhiyong Cui Kristian C. Henrickson Ruimin Ke Ziyuan Pu Yinhai Wang GNN AI4TS 33 736 0 20 Feb 2018