def communities(self, G):
# Identify the largest maximal clique: largest_max_clique
largest_max_clique = set(
sorted(nx.find_cliques(G), key=lambda x: len(x))[-1])
# Create a subgraph from the largest_max_clique: G_lmc
G_lmc = G.subgraph(largest_max_clique).copy()
# Go out 1 degree of separation
for node in list(G_lmc.nodes()):
G_lmc.add_nodes_from(G.neighbors(node))
G_lmc.add_edges_from(
zip([node] * len(list(G.neighbors(node))), G.neighbors(node)))
# Record each node's degree centrality score
for n in G_lmc.nodes():
G_lmc.node[n]['degree centrality'] = nx.degree_centrality(G_lmc)[n]
# Create the ArcPlot object: a
a = ArcPlot(G_lmc, node_order='degree centrality')
# Draw the ArcPlot to the screen
a.draw()
plt.show()
def draw_graph(graph):
# pos = graphviz_layout(graph, prog='twopi', args='')
# plt.figure(figsize=(8, 8))
# nx.draw(graph, pos, node_size=20, alpha=0.5, node_color="blue", with_labels=False)
# plt.axis('equal')
# plt.show()
# options = {
# 'node_color': 'black',
# 'node_size': 50,
# 'line_color': 'grey',
# 'linewidths': 0,
# 'width': 0.1,
# }
# nx.draw(graph, **options)
# plt.show()
# Assume we have a professional network of physicians belonging to hospitals.
# c = CircosPlot(graph, node_color='university', node_grouping='university')
c = ArcPlot(graph,
node_color="country",
node_grouping="university",
group_order="alphabetically")
c.draw()
plt.show() # only needed in scripts
def arc_plotting(self, G):
# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the ArcPlot object: a
a = ArcPlot(graph=G, node_order='degree')
# Draw the ArcPlot to the screen
a.draw()
plt.show()
################################## Task 2 (ArcPlot)
# Import necessary modules
from nxviz.plots import ArcPlot
import matplotlib.pyplot as plt
# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the ArcPlot object: a
a = ArcPlot(graph=G, node_order='degree')
# Draw the ArcPlot to the screen
a.draw()
plt.show()
############################### Task 3 (CircosPlot)
# Import necessary modules
from nxviz import CircosPlot
import matplotlib.pyplot as plt
# Iterate over all the nodes, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the CircosPlot object: c
c = CircosPlot(G,
"""
Displays a NetworkX lollipop graph to screen using a ArcPlot.
"""
import matplotlib.pyplot as plt
import networkx as nx
import numpy.random as npr
from nxviz.plots import ArcPlot
G = nx.lollipop_graph(m=10, n=4)
for n, d in G.nodes(data=True):
G.node[n]['value'] = npr.normal()
c = ArcPlot(G, node_color='value', node_order='value')
c.draw()
plt.show()
'''
# Import necessary modules
from nxviz import MatrixPlot
import matplotlib.pyplot as plt
# Calculate the largest connected component subgraph: largest_ccs
largest_ccs = sorted(nx.connected_component_subgraphs(G), key=lambda x: len(x))[-1]
# Create the customized MatrixPlot object: h
h = MatrixPlot(graph=largest_ccs, node_grouping='grouping')
# Draw the MatrixPlot to the screen
h.draw()
plt.show()
'''
# Import necessary modules
from nxviz.plots import ArcPlot
import matplotlib.pyplot as plt
# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
# Calculate the degree of each node: G.node[n]['degree']
G.node[n]['degree'] = nx.degree(G, n)
# Create the ArcPlot object: a
a = ArcPlot(graph=G, node_order='degree')
# Draw the ArcPlot to the screen
a.draw()
plt.show()
