FrequentSubgraphMining

Gaston Graph Mining with Python This is a python implementation of the Gaston graph mining algorithm.

Author: Colin Conduff

"Gaston finds all frequent subgraphs by using a level-wise approach in which first simple paths are considered, then more complex trees and finally the most complex cyclic graphs. It appears that in practice most frequent graphs are not actually very complex structures; Gaston uses this quickstart observation to organize the search space efficiently. To determine the frequency of graphs, Gaston employs an occurrence list based approach in which all occurrences of a small set of graphs are stored in main memory."

• Siegfried Nijssen (Gaston author)

Software Requirements:

• python 3.2 or later
• networkx 1.11
• matplotlib

Installation: cd gaston_py pip install .

Command Line Interface: For usage instructions, use the following command. gaston -h

Examples: gaston 0.95 test_files/medium_chemical.txt -o output_files/ -c -t gaston 6 test_files/medium_chemical.txt gaston 2 test_files/Chemical_340.txt

Notes:

• Support is defined as frequency(subgraph) / count(graphs). See reference [1] below for details.
• If an output directory is provided:
  • Frequent subgraphs are drawn using matplotlib and saved under [output folder]/graphs/.
  • A line_graph.txt file is generated containing the frequent subgraphs in Line Graph format.
• Test files are available in the test_files directory. The Chemical_340 dataset was obtained from Nijssen and Kok's website.

Run Tests: python setup.py test

Performance:

• This Python implementation is significantly slower than the original implementation in C++ by Nijssen and Kok, as well as the Java implementation by the ParSeMis library.
• It is also much slower than gSpan impelementations in Python. This is likely due to the use of an occurrence list, which tracks all subgraphs that have been seen to ensure the correctness of the algorithm.

References: [1] Siegfried Nijssen and Joost Kok. A Quickstart in Frequent Structure Mining Can Make a Difference. Proceedings of the SIGKDD, 2004. http://liacs.leidenuniv.nl/~nijssensgr/gaston/index.html

Additional Resources: Java implementation of the Gaston algorithm https://www2.cs.fau.de/EN/research/zold/ParSeMiS/index.html https://github.com/timtadh/parsemis