/tools/S-CGIB
S-CGIB
NSLab-CUK/S-CGIB
summary
S-CGIB is a pre-training architecture for Graph Neural Networks aimed at predicting molecular properties without human annotations. It utilizes self-supervised learning to generate graph-level representations and has been tested on various molecular datasets.
description
Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure PyTorch backend.
topics
graphgraph-foundation-modelgraph-neural-networksgraph-representation-learningmolecular-graph-learningmolecular-property-predictiongraph-information-bottleneckpretrained-graph-modelself-supervised-graph-learning
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