cite contains implementation for our paper. If you find this code useful in your research, please consider citing:
@misc{shaul2021cgspan,
title={cgSpan: Closed Graph-Based Substructure Pattern Mining},
author={Zevin Shaul and Sheikh Naaz},
year={2021},
eprint={2112.09573},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
cgSpan is an algorithm for mining closed frequent subgraphs. This implementation of cgSpan is built using an existing implementation for gSpan.
gSpan is an algorithm for mining frequent subgraphs.
This program implements cgSpan with Python. The repository on GitHub is https://github.com/NaazS03/cgSpan
The gSpan implementation referenced by this program can be found on GitHub at https://github.com/betterenvi/gSpan.
This program supports undirected graphs.
This program supports Python 3.
Install this project using pip:
pip install cgspan-mining
First, clone the project:
git clone https://github.com/NaazS03/cgSpan.git
cd cgSpan
You can optionally install this project as a third-party library so that you can run it under any path.
python setup.py install
The command is:
python -m cgspan_mining [-s min_support] [-n num_graph] [-l min_num_vertices] [-u max_num_vertices] [-v True/False] [-p True/False] [-w True/False] [-h] database_file_name
- Read graph data from ./graphdata/graph.data, and mine closed subgraphs given min support is 5000
python -m cgspan_mining -s 5000 ./graphdata/graph.data
- Read graph data from ./graphdata/graph.data, mine closed subgraphs given min support is 5000, and visualize these frequent subgraphs(matplotlib and networkx are required)
python -m cgspan_mining -s 5000 -p True ./graphdata/graph.data
- Print help info
python -m cgspan_mining -h
cgSpan: Closed Graph-Based Substructure Pattern Mining, by Zevin Shaul and Sheikh Naaz
CloseGraph: Mining Close Frequent Graph Patterns, by X. Yan and J.Han.
gSpan: Graph-Based Substructure Pattern Mining, by X. Yan and J. Han. Proc. 2002 of Int. Conf. on Data Mining (ICDM'02).