A research-grade alternative to standard GNN libraries for training deep graph neural networks. It implements residual connections and dilated convolutions to solve the vanishing gradient problem in large-scale graph learning.
Tech Stack
PythonAI, ML & Data
Why deep_gcns_torch?
• State-of-the-art performance
• Supports very deep architectures
• Optimized for PyTorch
Limitations
• High GPU memory requirements
• Academic focus
• Complex implementation
3/3/2026
Last Update
154
Forks
7
Issues
MIT
License
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