Question

So I have this code from the networkx example, but I’m trying to figure out how to limit node within a radius ‘r’ in order to graph a random geometric graph within the bounds of a circle. I know how I would do it logic-wise, but I’m a bit confused how everything works and have been trying to figure it out on my own with no solution so far. Thanks for the help!

import networkx as nx
import matplotlib.pyplot as plt

G = nx.random_geometric_graph(1000,0.1)

# position is stored as node attribute data for random_geometric_graph
pos = nx.get_node_attributes(G,'pos')

# find node near center (0.5,0.5)
dmin =1
ncenter =0
for n in pos:
    x,y = pos[n]
    d = (x-0.5)**2+(y-0.5)**2
    if d<dmin:
        ncenter = n
        dmin = d

# color by path length from node near center
p = nx.single_source_shortest_path_length(G,ncenter)

plt.figure(figsize=(8,8))
#node_color=p.values()
nx.draw_networkx_edges(G,pos,nodelist=[ncenter],alpha=0.4)
nx.draw_networkx_nodes(G,pos,nodelist=p.keys(),
                   node_size=80,
                   node_color='#0F1C95',
                   cmap=plt.cm.Reds_r)

plt.xlim(-0.05,1.05)
plt.ylim(-0.05,1.05)
plt.axis('off')
plt.savefig('random_geometric_graph.png')
plt.show()

Answer 1

You could use a dict comprehension such as

p = {node:length for node, length in nx.single_source_shortest_path_length(G,ncenter).items()
     if length < 5}

to limit the dict to those nodes whose distance from ncenter is < 5.

For Python2.6 or older, you could use

p = dict((node, length) for node, length in nx.single_source_shortest_path_length(G,ncenter).items()
     if length < 5)

---

You could also replace

dmin =1
ncenter =0
for n in pos:
    x,y = pos[n]
    d = (x-0.5)**2+(y-0.5)**2
    if d<dmin:
        ncenter = n
        dmin = d

with a one-liner:

ncenter, _ = min(pos.items(), key = lambda (node, (x,y)): (x-0.5)**2+(y-0.5)**2)

---

To draw only those nodes whose distance from ncenter is < 5, define the subgraph:

H = G.subgraph(p.keys())    
nx.draw_networkx_edges(H, pos, alpha = 0.4)
nx.draw_networkx_nodes(H, pos, node_size = 80, node_color = node_color,
                       cmap = plt.get_cmap('Reds_r'))

---

import networkx as nx
import matplotlib.pyplot as plt
G = nx.random_geometric_graph(1000, 0.1)

# position is stored as node attribute data for random_geometric_graph
pos = nx.get_node_attributes(G, 'pos')

# find node near center (0.5,0.5)
ncenter, _ = min(pos.items(), key = lambda (node, (x, y)): (x-0.5)**2+(y-0.5)**2)

# color by path length from node near center
p = {node:length
     for node, length in nx.single_source_shortest_path_length(G, ncenter).items()
     if length < 5}

plt.figure(figsize = (8, 8))
node_color = p.values()
H = G.subgraph(p.keys())    
nx.draw_networkx_edges(H, pos, alpha = 0.4)
nx.draw_networkx_nodes(H, pos, node_size = 80, node_color = node_color,
                       cmap = plt.get_cmap('Reds_r'))

plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.axis('off')
plt.savefig('random_geometric_graph.png')
plt.show()