Game Clustering
Dr. CHAN Chung
Ali Al-Bashabsheh
Handason Tam
And big thanks to
Zhao Chao
And thanks also to the implementation of Incremental Breadth-First Search
"Maximum Flows By Incremental Breadth-First Search" A.V. Goldberg, S.Hed, H. Kaplan, R.E. Tarjan, and R.F. Werneck
- install anaconda/miniconda if you haven't: https://conda.io/docs/user-guide/install/index.html
$ git clone https://github.com/handasontam/Game-Clustering.git
$ conda create --name gameclustering python=3.5 # create virtual conda environment
$ source activate gameclustering
$ pip install -r requirements.txt
$ python run_experiment.py --cpu 2 --data data/example_graph.txt --output /tmp --undirected --unweighted --beta 0.5 # example
$ python run_experiment.py -h
usage: run_experiment.py [-h] --cpu CPU --data DATA
--output OUTPUT [--directed]
[--undirected] [--weighted]
[--unweighted] --beta BETA
optional arguments:
-h, --help show this help message and exit
--cpu CPU number of cpu core to run the algorithm in parallel
--data DATA the file path of the graph data
--output OUTPUT the output directory
--directed
--undirected
--weighted
--unweighted
--beta BETA the beta value to use, range: [0,1]
# data/example_graph.txt
1 2 1
3 4 1
4 5 1
5 3 1
$ python run_experiment.py --cpu 2 --data data/example_graph.txt --output /tmp --undirected --weighted --beta 0.5