!!! DEPRECATED !!! - Please refer to (https://github.com/sqrhussain/homophily-community-gnn)

Network structure impact on graph neural networks

Dependencies: to be added

Papers

This code is the basis for our

• On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
• The Interplay between Communities and Homophily in Semi-supervised Classification Using Graph Neural Networks - a preprint is currently available

Citation

If you use our code/paper please cite our work :)

@inproceedings{hussain2020impact,
  title={On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks},
  author={Hussain, Hussain and Duricic, Tomislav and Lex, Elisabeth and Kern, Roman and Helic, Denis},
  booktitle={International Conference on Complex Networks and Their Applications},
  pages={15--26},
  year={2020},
  organization={Springer}
}

Datasets

First, run python -m src.data.dataset_handle to download and transform the datasets. This works for Cora, Citeseer, WebKB and Pubmed without hassle. Needs some tweaks to work on other datasets (to be fixed/explained).

Generating synthetic graphs from the original ones

Configuration model

Eliminates community structure while keeping the degree sequence.

Use python -m src.data.create_configuration_model

Stochastic block model

Eliminates the skew in the degree distribution (approaches a binomial distribution) while aiming to preserve the community structure using Louvain method for community detection.

Use python -m src.data.create_configuration_model

Erdős–Rényi model

Eliminates the community structure and turns the degree distribution into a binomial distribution. The only preserved properties are the node sequence (number, identity and features) and an approximate edge density.

Use python -m src.data.create_random_graph

Hyperparameter optimization

Run python -m src.hyperparam_search with the suitable parameters. You can modify the ranges within the python file. Stores validation results in reports/results/eval/ which will be necessary to run train.py.

Training and evaluating

Run python -m src.train with the suitable parameters. This file uses the resutls from the previous step and stores the evaluation resutls in reports/results/test_acc.

Computing the uncertainty coefficient

View the notebooks/Uncertainty coefficient.ipynb for details