14 citations · 26 across the 6 of their papers we have counts for
6 papers
Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution
Yingqi Feng, Yufei Tang, Min Shi +1
Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups or consists of multiple obje…
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
Yu Huang, James Li, Min Shi +5
Ocean current, fluid mechanics, and many other spatio-temporal physical dynamical systems are essential components of the universe. One key characteristic of such systems is that c…
Evolutionary Architecture Search for Graph Neural Networks
Min Shi, David A. Wilson, Xingquan Zhu +4
Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen…
Topology and Content Co-Alignment Graph Convolutional Learning
Min Shi, Yufei Tang, Xingquan Zhu
In traditional Graph Neural Networks (GNN), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learni…
Multi-Label Graph Convolutional Network Representation Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with…
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have be…