39 citations · 75 across the 7 of their papers we have counts for
3 papers · 1 filter
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View
Jingcan Duan, Siwei Wang, Pei Zhang +5
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture ano…
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Jong Youl Choi, Pei Zhang, Kshitij Mehta +2
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecu…
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve +1
We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic m…