GraphMAE
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
About
GraphMAE is designed for generative self-supervised learning on graph data, achieving competitive performance in tasks such as node classification, graph classification, and molecular property prediction. It is implemented in Python and utilizes PyTorch, making it suitable for researchers and practitioners in deep learning and graph neural networks. The method supports various datasets and provides scripts for quick execution of different classification tasks.
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- Not stated
- Last commit
- 3 years ago
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THUKEG
Open-source projects on GitHub: Slime, AgentBench and P-tuning-v2
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