2 code implementations • 24 May 2020 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #1 on Univariate Time Series Forecasting on Electricity
8 code implementations • 31 May 2019 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #5 on Traffic Prediction on NE-BJ
5 code implementations • 3 Jan 2019 • Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
1 code implementation • 2 Mar 2022 • Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang
We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data.
1 code implementation • 25 Aug 2021 • Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
1 code implementation • 10 Jul 2021 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Second, the bandwidth of existing graph convolutional filters is fixed.
no code implementations • 27 Sep 2021 • Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.
no code implementations • 25 Nov 2021 • Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.