57 citations · 138 across the 11 of their papers we have counts for
5 papers · 2 filters
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Zhao Xu, Youzhi Luo, Xuan Zhang +7
Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies…
Group Contrastive Self-Supervised Learning on Graphs
Xinyi Xu, Cheng Deng, Yaochen Xie +1
We study self-supervised learning on graphs using contrastive methods. A general scheme of prior methods is to optimize two-view representations of input graphs. In many studies, a…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Yaochen Xie, Zhao Xu, Jingtun Zhang +2
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new…