python - Group vertices in clusters using NetworkX -

i trying represent graphically graphs, , need group in clusters nodes have common characteristics.

i using networkx, , need similar graph this tutorial, slide 44, left figure.

i want draw delimiting line around each cluster. current code that:

    vec = self.colors     colors = (linspace(0,1, len (set (vec)))* 20 + 10)     nx.draw_circular(g, node_color=array([colors[x] x in vec]))     show () 

i wish find example , see how can use networkx cluster graph.

i'm not positive question is. think you're asking "how networkx put nodes close together"

before launch answer, drawing documentation networkx here: http://networkx.lanl.gov/reference/drawing.html

so figure you're asking has 4 different communities clustered based on having lots of edges within each community , not many outside.

if don't want put effort it, spring_layout putting tightly knit communities together. basic algorithm of spring_layout acts if edges springs (and nodes repel). lots of edges keeps nodes close together. note initializes positions randomly, each time you'll different output.

the easiest way just

nx.draw_spring(g) 

but maybe want more. if want to, can fix every single node's position. define dict, named pos.

pos = {} node in g.nodes_iter():     pos[node] = (xcoord, ycoord). 

where xcoord , ycoord coordinates want node at.

then draw_networkx(g, pos = pos)

that's lot of effort. tell few of them have in particular places, , let networkx rest

define fixedpos few nodes , run spring_layout telling nodes fixed , giving fixedpos initial positions. hold fixed , fit else around.

here code generates network has 4 connected parts , few other edges between them. (actually generates complete network , deletes few edges between these parts). draws simple spring layout. fixes 4 of them @ corners of square , places other nodes around fixed positions.

import networkx nx import random import pylab py math import floor  g = nx.complete_graph(20)  edge in g.edges():     if floor(edge[0]/5.)!=floor(edge[1]/5.):         if random.random()<0.95:             g.remove_edge(edge[0],edge[1])   nx.draw_spring(g) py.show()   fixedpos = {1:(0,0), 6:(1,1), 11:(1,0), 16:(0,1)} pos = nx.spring_layout(g, fixed = fixedpos.keys(), pos = fixedpos)  nx.draw_networkx(g, pos=pos)  py.show() 

you can specify weights edges, pass weights spring_layout , larger weights tell keep corresponding nodes closer together. once you've identified communities, increase weights within communities/clusters if necessary keep them close together.

note can specify color make each node, straightforward specify color each community/cluster.

if want draw curves around each of clusters, you'll have through matplotlib.