This code adopts the framework of the code of ClusterNet method in the NeurIPS 2019 paper "End to End Learning and Optimization on Graphs" and adds the option to use GAP method paper "GAP: Generalizable Approximate Graph Partitioning Framework" for cellular neighborhood identification.

@inproceedings{wilder2019end,
  title={End to End Learning and Optimization on Graphs},
  author={Wilder, Bryan and Ewing, Eric and Dilkina, Bistra and Tambe, Milind},
  booktitle={Advances in Neural and Information Processing Systems},
  year={2019}
}

@article{nazi2019gap,
  title={Gap: Generalizable approximate graph partitioning framework},
  author={Nazi, Azade and Hang, Will and Goldie, Anna and Ravi, Sujith and Mirhoseini, Azalia},
  journal={arXiv preprint arXiv:1903.00614},
  year={2019}
}

• experiments_singlegraph.py runs the experiments which do link prediction on a given graph. See below for parameters.
• experiments_inductive.py runs the experiments which evaluate training and generalization on a distribution of graphs.
• models.py contains the definitions for both the ClusterNet model (GCNClusterNet) as well as various models used for the baselines.
• modularity.py contains helper functions and baseline optimization algorithms for the community detection task.
• kcenter.py contains helper functions and baseline optimization algorithms for the facility location task.
• loss_functions.py contains definitions of the loss function used to train ClusterNet and GCN-e2e for both tasks.
• utils.py contains helper functions to load/manipulate the datasets.

The datasets used are placed in the mydata folder, which are knn-graphs of different k and cell type information as node features (feat). The hlt data can be downloaded from https://drive.google.com/drive/folders/1OImf7SiSwqVJz7HYCGk71Vz7P5lZozFu?usp=share_link.

See run.sh for all commands to reproduce the results of ClusterNet and GAP.

The Dockerfile in the main directory builds the environment that was used to run the original experiments, with the exception of pygcn, which needs to be downloaded and installed separately. For reference, the individual dependencies are:

• PyTorch (tested on version 1.2)
• networkx (tested on version 2.3)
• igraph (tested on version 0.7.1). This is optional; only used to accelerate pre-processing operations.
• pygcn
• sklearn (tested on version 0.21.3)
• numpy (tested on version 1.16.5)