BrainGB is a python package for testing Graph Neural Networks on Brain Networks.
To install BrainGB as a package, simply run
pip install BrainGB
Alternatively, you can also download the repository from Github. The main package is under the src folder. If you choose to go with this method, please check the Specification of Dependencies section for dependency requirements.
Specification of Dependencies
BrainGB depends on the following frameworks:
torch~=1.10.2 numpy~=1.22.2 nni~=2.4 PyYAML~=5.4.1 scikit-learn~=1.0.2 networkx~=2.6.2 scipy~=1.7.3 tensorly~=0.6.0 pandas~=1.4.1 libsvm~=22.214.171.124 matplotlib~=3.4.3 tqdm~=4.62.3 torch-geometric~=2.0.3 h5py~=3.6.0
To install the dependencies, run:
pip install -r requirements.txt
Notice that if you install the package through pip, the dependencies are automatically installed.
To import the models detailed in the paper:
from BrainGB import GAT, GCN, BrainNN, GCN
The BrainNN is required and will be served as the parent module of the GAT, GCN models. You may choose either GAT or GCN as the submodule.
To initialize a GCN model
sample: Data = Data() # A torch geometric data num_features = data.x.shape num_nodes = data.x.shape gcn_model = GCN(num_features, num_nodes) model = BrainNN(args.pooling, gcn_model, MLP(2 * num_nodes))
To initialize a GAT model, simply replace the GCN with GAT. Both models are customizable. Please refer to the Advanced Usage page for more details.
Running Example Scripts
The repository also comes with example scripts. To train our model on any of the datasets we tested, simply run:
python -m main.example_main --dataset_name=<dataset_name> [--model_name=<model_name> --gcn_mp_type=<mp_mechanism> --gat_mp_type=<attention_mp_mechanism> --node_features=<feature_name> --pooling=<pooling_name> --n_GNN_layer=<GNN_num> --n_MLP_layers=<MLP_num> --hidden_dim=<hidden_layer_dimension> --epochs=<epoch_num> --k_fold_splits=<split_num> --test_interval=<evaluation_interval_num>]
dataset_name is the name of the dataset to use (required parameter). We include the following four datasets in our paper:
You can also construct your own datasets by following the instructions on neuroimaging preprocessing and functional or structural brain network construction on our website.
Please place the dataset files in the
datasets folder under the package examples folder. Create the folder if it does not exist.
model_name specifies the backbone model type. Choose
gcn to test the message passing variants without attention and
gat to test the attention-enhanced message passing mechanisms. Specifically, use
gcn_mp_type to set a message vector design and use
gat_mp_type to set an attention-enhancing mechanism.
node_features specifies the artificial node feature initialization for each brain region.
pooling specifies the pooling strategy to get a graph-level representation for each subject.
You can also change other hyper-parameters, such as
--epochs, etc., to adjust the detailed model design or control the training process. All those hyper-parameters can be automatically searched and optimized using the AutoML tool NNI by passing
We welcome contributions to the package. Please feel free to open an issue or pull request.