/Graph-in-Graph-Neural-Network

Graph in Graph Neural Network (https://arxiv.org/abs/2407.00696)

Primary LanguageJupyter Notebook

Codes in this repository consist of GIG-GatedGCN and GIG-CTR-GCN. The original GatedGCN can be found from here and orginal CTR-GCN can be found from here. If you find our research is supportive, please cite:

@misc{wang2024graphgraphneuralnetwork,
      title={Graph in Graph Neural Network}, 
      author={Jiongshu Wang and Jing Yang and Jiankang Deng and Hatice Gunes and Siyang Song},
      year={2024},
      eprint={2407.00696},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2407.00696}, 
}

Requirements

  • Python 3.8

  • PyTorch 1.9.0

  • CUDA 11.1

  • DGL 0.9.0

  • Check the required python packages in ga_dependency.txt right here for graph analysis tasks.

pip install -r ga_dependency.txt
  • Check the required python packages in sk_dependency.txt right here for action recognition tasks.
pip install -r sk_dependency.txt

Training

For general graph analysis tasks (clicking the link will directly take you to the target folder):

  1. Execute the code blocks in 'prepare_superpixels_MNIST.ipynb' to download dataset and complete preprocessing
  2. Execute the 'MNIST.ipynb' to start training
  1. Execute the code blocks in 'prepare_superpixels_CIFAR.ipynb' to download dataset and complete preprocessing
  2. Execute the 'CIFAR10.ipynb' to start training
  1. Execute the code blocks in 'prepare_SBM_CLUSTER.ipynb' to download dataset and complete preprocessing
  2. Execute the 'CLUSTER.ipynb' to start training
  1. Execute the code blocks in 'prepare_SBM_PATTERN.ipynb' to download dataset and complete preprocessing
  2. Execute the 'PATTERN.ipynb' to start training
  1. Execute the code blocks in 'prepare_molecules.ipynb' to download dataset and complete preprocessing
  2. Execute the 'ZINC.ipynb' to start training
  1. Execute the code blocks in 'prepare_molecules_ZINC_full.ipynb' to download dataset and complete preprocessing
  2. Execute the 'ZINC-full.ipynb' to start training
  1. Execute the code blocks in 'prepare_molecules.ipynb' to download dataset and complete preprocessing
  2. Execute the 'AQSOL.ipynb' to start training
  1. Execute the code blocks in 'prepare_TSP.ipynb' to download dataset and complete preprocessing
  2. Execute the 'TSP.ipynb' to start training
  1. Dataset will be automatically downloaded and processed. Nonthing needs to be done right here.
  2. Execute the 'TU-GIG-Codes.ipynb' to start training
  1. Dataset will be automatically downloaded and processed. Nonthing needs to be done right here.
  2. Execute the 'OGBG-PPA.ipynb' to start training

For human-skeleton action recognition tasks, the raw data should be downloaded from CTR-GCN (https://github.com/Uason-Chen/CTR-GCN). Then data preprocessing should be run then as following:

Directory Structure
  • Put downloaded data into the following directory structure:
- data/
  - NW-UCLA/
    - all_sqe
      ... # raw data of NW-UCLA
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...
Generating Data
  • Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:
 cd ./data/ntu # or cd ./data/ntu120
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python seq_transformation.py
Executing training

  • NTURGB+D X-Sub

    • Using command python main.py --config config/nturgbd-cross-subject/default.yaml --work-dir work_dir/ntu/csub/ctrgcn --device 0

  • NTURGB+D X-View

    • Using command python main.py --config config/nturgbd-cross-view/default.yaml --work-dir work_dir/ntu/cview/ctrgcn --device 0