def matrix_plotting(self, G):
# 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)
# Draw the MatrixPlot to the screen
h.draw()
plt.show()
list(nx.connected_component_subgraph(G))
for g in list(nx.connected_component_subgraph(G)):
print(len(g.nodes()))
################################# Task 1 (MatrixPlot)
# 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()
################################## 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)
# MatrixPlot visualization of the largest connected component subgraph,
# with authors grouped by their user group number.
# ### Task IMplementation ######
# Create the MatrixPlot object h. You have to specify the parameters
# "graph" and "node_grouping" to be the largest connected component subgraph
# and 'grouping', respectively.
# 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(largest_ccs, 'grouping')
# Draw the MatrixPlot to the screen
h.draw()
plt.show()
#%%
# ########## Arc Plot ###########
# Make an ArcPlot of the GitHub collaboration network,
# with authors sorted by degree. To do this:
# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
def test_matrix_plot():
m = MatrixPlot(G) # noqa: F841
diff = diff_plots(m, "matrix.png", baseline_dir, result_dir)
assert diff is None
## Quick draw of G
nx.draw(G, with_labels=True)
plt.show()
# Graph properties
G.nodes()
len(G.edges())
len(G.nodes())
## Degree centrality and betweenne centrality
nx.degree_centrality(G) # Returns a dictionary
plt.hist(list(nx.degree_centrality(G).values()))
plt.show()
nx.betweenness_centrality(G)
plt.hist(list(nx.betweenness_centrality(G).values()))
plt.show()
# Visualizatio
largest_ccs = sorted(nx.connected_component_subgraphs(G),
key=lambda x: len(x))[-1]
h = MatrixPlot(graph=G)
h.draw()
plt.show()
nx.degree(G)
nx.draw(G, with_labels=True)
plt.show()
def test_matrix_plot():
m = MatrixPlot(G) # noqa: F841
from collections import Counter
mf_count = Counter([gender[i - 1] for i in g1.nodes()])
def test_answer(mf_counts):
assert mf_counts['female'] == 17
assert mf_counts['male'] == 12
test_answer(mf_count)
from nxviz import MatrixPlot
m = MatrixPlot(g)
m.draw()
plt.show()
from nxviz import ArcPlot
a = ArcPlot(g)
a.draw()
from nxviz import CircosPlot
c = CircosPlot(g)
c.draw()
plt.show()
# plt.savefig('images/seventh.png', dpi=300)
def test_matrix_plot():
m = MatrixPlot(G)
from itertools import combinations
from collections import defaultdict
graph = pickle.load(open('github_users.p', 'rb'))
print("no. of users: " + str(len(graph.nodes())))
print("no. of user-collaborations(p2p) : " + str(len(graph.edges())))
plt.hist(list(nx.degree_centrality(graph).values()))
plt.show()
# Calculate the largest connected component subgraph
largest_ccs = sorted(nx.connected_component_subgraphs(graph),
key=lambda x: len(x))[-1]
h = MatrixPlot(largest_ccs)
h.draw()
plt.show()
for n, d in graph.nodes(data=True):
graph.node[n]['degree'] = nx.degree(graph, n)
# a = ArcPlot(graph=graph, node_order='degree')
# a.draw()
# plt.show()
# Calculate the maximal cliques in G
cliques = nx.find_cliques(graph)
#obseravation:- we can clearly observe positive correlation between degrees and degree centrality of network
# Compute the betweenness centrality of G: bet_cen
bet_cen = nx.betweenness_centrality(G)
# Create a scatter plot of betweenness centrality and degree centrality
plt.scatter(list(bet_cen.values()), list(deg_cent.values()))
# Display the plot
plt.show()
# 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)
# Draw the MatrixPlot to the screen
h.draw()
plt.show()
# 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