An attempt to determine important clusters in a graph. Original algorithm based on: Bridging Centrality: Graph Mining from Element Level to Group Level @see http://dl.acm.org/citation.cfm?id=1401934 The style guide follows the strict python PEP 8 guidelines. @see http://www.python.org/dev/peps/pep-0008/ ============================================ Arguments for python main.py ============================================ The following are arguments required: -i: the density threshold. -o: the output file. -v: the bridge cut version (vertex-c, vertex-b, edge-b, edge-c). -t: the density threshold. ============================================ Execution ============================================ Execution is straightforward. After choosing a density threshold (-t), a version (-v), and an input file (-i) the program will spit out the clusters to the output file (-o). ====================== Usage ====================== The following are some example use cases. > python main.py -i "../data/toy/toy-bowtie.txt" -o "../results/toy/toy-bowtie.txt" -d 0 -v "edge-c" -t .5