This repository serves as a mini-tutorial on using self-supervised learning for graphs. Self-supervised learning is a class of unsupervised machine learning methods where the goal is to learn rich representations of unstructured data when we do not have access to any labels. This repository implements a variety of commonly used methods (augmentations, encoders, loss functions) for self-supervised learning on graphs. The codebase also includes the option of loading commonly used graph datasets for a variety of downstream tasks. It is built using PyTorch Geometric which is a library built on PyTorch for graph machine learning.
Setup the conda environment which ensures the installation of the correct version of PyTorch Geometric (PyG) and all other dependencies.
conda env create -f environment.yml
conda activate graphssl
Clone this repository.
git clone https://github.com/paridhimaheshwari2708/GraphSSL.git
cd GraphSSL/
For training a self-supervised model, we use the run.py script and we need to specify a few important arguments:
--save Specify where folder name of the experiment where the logs and models are saved
--dataset Specify the dataset on which you want to train the model
--model Specify the model architecture of the GNN Encoder
--feat_dim Specify the dimension of node features in GNN
--layers Specify the number of layers of GNN Encoder
--loss Specify the loss function for contrastive training
--augment_list Specify the augmentations to be applied as space separated strings
The options supported for above arguments are:
| Argument | Choices |
|---|---|
| dataset | proteins, enzymes, collab ,reddit_binary, reddit_multi, imdb_binary, imdb_multi, dd, mutag, nci1 |
| model | gcn, gin, resgcn, gat, graphsage, sgc |
| loss | infonce, jensen_shannon |
| augment_list | edge_perturbation, diffusion, diffusion_with_sample, node_dropping, random_walk_subgraph, node_attr_mask |
As an example, run the following command to train a self-supervised model on the proteins dataset
python3 run.py --save proteins_exp --dataset proteins --model gcn --loss infonce --augment_list edge_perturbation node_dropping
For training and evaluating the model on the downstream task, here graph classification, we use the run_classification.py script. The arguments are:
--save Specify where folder name of the experiment where the logs and models are saved
--load Specify the folder name from which we want the self-supervised model is loaded
If present, the GNN loads the pretrained weights and only the classifier head is trained
If left empty, the model will be trained end-to-end without self-supervised learning
--dataset Specify the dataset on which you want to train the model
--model Specify the model architecture of the GNN Encoder
--feat_dim Specify the dimension of node features in GNN
--layers Specify the number of layers of GNN Encoder
--train_data_percent Specify the fraction of training samples which are labelled
The options supported for above arguments are:
| Argument | Choices |
|---|---|
| dataset | proteins, enzymes, collab ,reddit_binary, reddit_multi, imdb_binary, imdb_multi, dd, mutag, nci1 |
| model | gcn, gin, resgcn, gat, graphsage, sgc |
| augment_list | edge_perturbation, diffusion, diffusion_with_sample, node_dropping, random_walk_subgraph, node_attr_mask |
As an example, run the following command to train the final classifier head of a self-supervised model on the proteins dataset
python3 run_classification.py --save proteins_exp_finetuned --load proteins_exp --dataset proteins --model gcn --train_data_percent 1.0
For the same dataset, training the model end-to-end (without self-supervised pretraining) can be done as follows
python3 run_classification.py --save proteins_exp_finetuned_e2e --dataset proteins --model gcn --train_data_percent 1.0
We have also created a Colab Notebook which combines various techniques in self-supervised learning and provides an easy-to-use interface for training your own models.
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