STOA
STOA implements a genetic algorithm which can be used for the extraction of navigationally optimized hierarchies (spanning trees) of networks. For details please refer to the associated publication [1].

The genetic algorithm and the scripts in the pythonscript directory require a working Java 1.7 as well as a Python 2.7 installation including the networkx and numpy packages.

Create navigational hierarchies:
The scripts needed for the genetic algorithm need to be placed in a subdirectory called pythonscripts. Currently these are:

• filehandler.py
• get-apsp.py
• get-spanning-trees.py
• stretch.py
• ufitness.py

To create navigational hierarchies run:
java -jar ga.jar networkfile
Use -h option to get help on the available options for the algorithm.

Network format:
Networks need to be represented as edge lists with continuous node ids starting at 1. The tool converter.jar can be used to transform arbitrary edge lists to this format. However, be sure to delete comments in the edge list file first.
java -jar converter.jar edgelistfile targetfilename

Since the genetic algorithm is working on spanning trees make sure to use connected networks. The largest connected component of a network can be extracted with the "get_largest_component.py" script. However, don't forget to convert the target file with the converter, afterwards.
python get_largest_component.py --network networkfile --filename targetfilename

The tool createDots.jar can be used to create .dot files (used by Graphviz) from the tree files created by the genetic algorithm:
java -jar createDots.jar treedirectory

A correctly formatted sample network file (the one used in the parametrization experiments) is included:
wiki-sample.txt

Additionally, various utility scripts are included in the pythonscript folder.

References

1. Stefan John, Michael Granitzer, Denis Helic. Extracting Navigation Hierarchies from Networks with Genetic Algorithms. In Proceedings of the 12th International Conference on Web Information Systems and Technologies.