README.md

Understanding GNNs and Homophily in Dynamic Node Classification

[AISTATS 2025] Understanding GNNs and Homophily in Dynamic Node Classification

Instructions for Reproducibility

1. In the generation directory, use the following command to download and preprocess datasets:

   source generate_graphs.sh

2. Once the datasets are downloaded and preprocessed, in the root directory, use the following command to train models and save results to the evaluation directory.

   source search.sh

3. After training the models and saving the results, load and visualize the results in the analysis_social_sgnn.ipynb or analysis_synthetic_sgnn.ipynb notebook located in the evaluation directory.

Citation

If you find this work useful, please cite our paper:

@inproceedings{ItoKW25dynamic,
  author    = {Michael Ito and Danai Koutra and Jenna Wiens},
  title     = {Understanding GNNs and Homophily in Dynamic Node Classification},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  publisher = {PMLR},
  year      = {2025},
}