cgspan-mining 1.0.1

Implementation of close frequent subgraph mining algorithm cgSpan

cgSpan

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.

Undirected Graphs

This program supports undirected graphs.

How to install

This program supports Python 3.

Method 1

Install this project using pip:

pip install cgspan-mining

Method 2

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

How to run

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 

Some examples

• 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

Reference

• CloseGraph Paper

CloseGraph: Mining Close Frequent Graph Patterns, by X. Yan and J.Han.

• gSpan Paper

gSpan: Graph-Based Substructure Pattern Mining, by X. Yan and J. Han. Proc. 2002 of Int. Conf. on Data Mining (ICDM'02).