128 citations · 234 across the 14 of their papers we have counts for
8 papers · 1 filter
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
Jie Chen, Zilong Li, Yin Zhu +2
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made,…
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing
Jie Chen, Weiqi Liu, Zhizhong Huang +3
The performance of GNNs degrades as they become deeper due to the over-smoothing. Among all the attempts to prevent over-smoothing, residual connection is one of the promising meth…
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Jie Chen, Shouzhen Chen, Junbin Gao +3
Due to the homophily assumption in graph convolution networks (GNNs), a common consensus in the graph node classification task is that GNNs perform well on homophilic graphs but ma…
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications
Mingyuan Bai, S. T. Boris Choy, Junping Zhang +1
The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., da…
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
Graph neural networks (GNN) have been ubiquitous in graph node classification tasks. Most of GNN methods update the node embedding iteratively by aggregating its neighbors' informa…
STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation
Yiqun Liu, Shouzhen Chen, Lei Chen +4
Numerical Weather Prediction (NWP) can reduce human suffering by predicting disastrous precipitation in time. A commonly-used NWP in the world is the European Centre for medium-ran…
Towards a Precipitation Bias Corrector against Noise and Maldistribution
Xiaoyang Xu, Yiqun Liu, Hanqing Chao +5
With broad applications in various public services like aviation management and urban disaster warning, numerical precipitation prediction plays a crucial role in weather forecast.…