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
[email protected]