GREED Experiments

This repository contains the Jupyter notebooks used to run the experiments reported in the paper "GREED: A Neural Framework for Learning Graph Distance Functions" accepted at NeurIPS 2022. The main repository containing source code is here. The notebooks have paths and configurations which are custom to our setup, but these can easily be adapted to experiment in general settings.

Directory descriptions

1. nbs_train: train various models

2. nbs_pred: make predictions using the trained models

3. nbs_regress: regression experiments

4. nbs_rank: ranking experiments

5. nbs_range: range query experiments

6. nbs_index: retrieval experiments using index structures

Citation

@inproceedings{ranjan&al22,
  author = {Ranjan, Rishabh and Grover, Siddharth and Medya, Sourav and Chakaravarthy, Venkatesan and Sabharwal, Yogish and Ranu, Sayan},
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {GREED: A Neural Framework for Learning Graph Distance Functions},
  booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference
               on Neural Information Processing Systems 2022, NeurIPS 2022, November 29-Decemer 1, 2022},
  year = {2022},
}