This repository provides resources on Graph Neural Network efficiency and scalability, as well as implementations of knowledge distillation techniques for developing resource-efficient GNNs.

Check out the accompanying paper 'On Representation Knowledge Distillation for Graph Neural Networks', which introduces new GNN distillation techniques using contrastive learning to preserve the global topology of teacher and student embeddings.

Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, and Chuan Sheng Foo. On Representation Knowledge Distillation for Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Special Issue on Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications.

PDF | Blog

❓New to GNN scalability: See awesome-efficient-gnns.md and the accompanying blogpost for a currated overview of papers on efficient and scalable Graph Representation Learning for real-world applications.

We benchmark the following knowledge distillation techniques for GNNs:

• Local Structure Preserving loss, Yang et al., CVPR 2020: preserve pairwise relationships over graph edges, but may not preserve global topology due to latent interactions.
• Global Structure Preserving loss, Joshi et al., TNNLS 2022: preserve all pairwise global relationships, but computationally more cumbersome.
• 🌟 Graph Contrastive Representation Distillation, Joshi et al., TNNLS 2022: contrastive learning among positive/negative pairwise relations across the teacher and student embedding spaces.
• 🔥 Your new GNN distillation technique?

We also include baselines: Logit-based KD, Hinton et al., 2015; and feature mimicking baselines for computer vision: FitNet, Romero et al., 2014, Attention Transfer, Zagoruyko and Komodakis, 2016.

We conduct benchmarks on large-scale and real-world graph datasets, where the performance gap between expressive+cumbersome teacher and resource-efficient student GNNs is non-negligible:

• Graph classification on MOLHIV from Open Graph Benchmark/MoleculeNet -- GIN-E/PNA teachers, GCN/GIN students.
• Node classification on ARXIV and MAG from Open Graph Benchmark and Microsoft Academic Graph -- GAT/R-GCN teachers, GCN/GraphSage/SIGN students.
• 3D point cloud segmentation on S3DIS -- not released publicly yet.
• Node classification on PPI -- provided to reproduce results from Yang et al.

Our results are reported with Python 3.7, PyTorch, 1.7.1, and CUDA 11.0. We used the following GPUs: RTX3090 for ARXIV/MAG, V100 for MOLHIV/S3DIS.

Usage instructions for each dataset are provided within the corresponding directory.

# Create new conda environment
conda create -n ogb python=3.7
conda activate ogb

# Install PyTorch (Check CUDA version!)
conda install pytorch=1.7.1 cudatoolkit=11.0 -c pytorch

# Install DGL
conda install -c dglteam dgl-cuda11.0

# Install PyG
CUDA=cu110
pip3 install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.1+${CUDA}.html
pip3 install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.1+${CUDA}.html
pip3 install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.1+${CUDA}.html
pip3 install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.1+${CUDA}.html
pip3 install torch-geometric

# Install other dependencies
conda install tqdm scikit-learn pandas urllib3 tensorboard
pip3 install ipdb, nvidia-ml-py3

# Install OGB
pip3 install -U ogb

@article{joshi2022representation,
  title={On Representation Knowledge Distillation for Graph Neural Networks},
  author={Chaitanya K. Joshi and Fayao Liu and Xu Xun and Jie Lin and Chuan-Sheng Foo},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022}
}

@article{joshi2022efficientgnns,
  author = {Joshi, Chaitanya K.},
  title = {Recent Advances in Efficient and Scalable Graph Neural Networks},
  year = {2022},
  howpublished = {\url{https://www.chaitjo.com/post/efficient-gnns/}},
}