As described in the paper, we run all of our non-timing experiments on a comination of local and cloud resources - either on a NVIDIA RTX A4000 or V100 GPU (each with 16GB of VRAM). All timing experiments were conducted on Google Cloud Platform with a V100 GPU. We perform 50 runs of Bayesian hyperparameter optimization using Weights and Biases on each CVI/dataset/task for each of the three methods. The best-performing hyperparameters for each CVI and dataset on node classification can be found in the config directory.

The code for CARL-G is in the src directory. train_carlg.py contains the code for the base CARL-G implementation, which trains and evaluates a model for node classification. train_carlg_clustering.py trains and evaluates CARL-G on node clustering and similarity search.

To run the model with the best hyperparameters, you can use the --flagfile flag. For example:

cd src
python train_carlg.py --flagfile=../config/sim_amazon-computers_class.cfg

to train and evaluate the model on the amazon-computers dataset for node classification tasks.

Many configurations of the models can be modified through the use of flags:

• --clustering_metric: This specifies the variant of CARL-G. The three possible values are:
  • silhouette: $\text{CARL-G}_{Sil}$ - the silhouette-based variant.
  • fast_silhouette: $\text{CARL-G}_{Sim}$- the faster simplified-silhouette-based variant.
  • vrc: $\text{CARL-G}_{VRC}$ - the VRC-based variant.
• --score_target: The target CVI score ($\tau$ in the paper).
• --num_clusters: The number of clusters ($c$ in the paper).
• --dataset: Which dataset to run the model on.

There are more configuration options that can be seen by viewing the code or by using the --helpfull flag.

We also include the weights for classification tasks in the weights directory, which can be directly loaded for evaluation purposes.

We used code from Link Prediction with Non-Contrastive Learning. We also used the evaluation portions of the code from the reference BGRL implementation and the reference AFGRL implementation.

If you use this code, please cite:

@article{shiao2023carl,
  title={CARL-G: Clustering-Accelerated Representation Learning on Graphs},
  author={Shiao, William and Saini, Uday Singh and Liu, Yozen and Zhao, Tong and Shah, Neil and Papalexakis, Evangelos E},
  journal={arXiv preprint arXiv:2306.06936},
  year={2023}
}