Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

This directory contains code necessary for running all the experiments.

#Requirements

Recent versions of Pytorch,Pytorch Geometric, numpy, scipy, sklearn, networkx and matplotlib are required.
You can install all the required packages using the following command:

$ conda create --name <env> --file requirements.txt

#Datasets Please download the Datasets files from https://rebrand.ly/mcsnet and replace the current dummy Datasets folder. This contains the original datasets, the dataset splits and other intermediate data dumps for reproducing tables and plots.

#Run Eperiments

The command lines to used for training models are listed commands.txt.
Command lines specify the exact hyperparameter settings used to train the models.

#Reproduce plots and figures

The notebooks folder contains .ipynb files which reproduce all the tables and figures presented in the paper.

Notes:

• GPU usage is required
• source code files are all in mcs folder.

If you find the code useful, please cite our paper:

@article{roy2022maximum,
  title={Maximum common subgraph guided graph retrieval: late and early interaction networks},
  author={Roy, Indradyumna and Chakrabarti, Soumen and De, Abir},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={32112--32126},
  year={2022}
}

Indradyumna Roy, Indian Institute of Technology - Bombay
indraroy15@cse.iitb.ac.in