Question

I’m trying to design a project that takes global positioning data, like city and state names along with latitudes and locations. I’ll also have distances between every pair of cities. I want to make a graph with all of this information, and manipulate it to perform some graph algorithms. I’ve decided to have city objects which contains each location’s data. Now should I have a hash function to differentiate objects? And how should I handle graph algorithms that combine nodes and remove edges?

def minCut(self):
    """Returns the lowest-cost set of edges that will disconnect a graph"""

    smcut = (float('infinity'), None)
    cities = self.__selectedcities[:]
    edges = self.__selectededges[:]
    g = self.__makeGRAPH(cities, edges)
    if not nx.is_connected(g):
        print("The graph is already diconnected!")
        return
    while len(g.nodes()) >1:
        stphasecut = self.mincutphase(g)
        if stphasecut[2] < smcut:
            smcut = (stphasecut[2], None) 
        self.__merge(g, stphasecut[0], stphasecut[1])
    print("Weight of the min-cut:  "+str(smcut[1]))

It’s in really bad shape. I’m rewriting my original program, but this is the approach i took from the previous version.

Answer 1

Depending on what version of networkx you have installed, there is a built-in implementation of min_cut available.

I had the 1.0RC1 package installed and that was not available.. but I upgraded to 1.4 and min_cut is there.

Here’s a (silly) example:

import networkx as nx
g = nx.DiGraph()
g.add_nodes_from(['London', 'Boston', 'NY', 'Dallas'])
g.add_edge('NY', 'Boston', capacity)
g.add_edge('Dallas', 'Boston')
g.add_edge('Dallas', 'London')
# add capacity to existing edge
g.edge['Dallas']['London']['capacity'] = 2
# create edge with capacity attribute
g.add_edge('NY', 'London', capacity=3)
print nx.min_cut(g, 'NY', 'London')