Extract the mean degree centrality day-by-day on the students partition
Here, you're going to see if the mean degree centrality over all nodes is correlated with the number of edges that are plotted over time. There might not necessarily be a strong correlation, and you'll take a look to see if that's the case.
This exercise is part of the course
Intermediate Network Analysis in Python
Exercise instructions
- Instantiate a new graph called
G_sub
containing a subset of edges. - Add nodes from
G
, including the node metadata. - Add in edges that fulfill the criteria, using the
.add_edges_from()
method. - Get the students projection
G_student_sub
fromG_sub
using thenx.bipartite.projected_graph()
function. - Compute the degree centrality of the students projection using
nx.degree_centrality()
(don't use the bipartite version). - Append the mean degree centrality to the list
mean_dcs
. Be sure to convertdc.values()
to a list first. - Hit 'Submit Answer' to view the plot!
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from datetime import datetime, timedelta
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
# Initialize a new list: mean_dcs
mean_dcs = []
curr_day = dayone
td = timedelta(days=2)
while curr_day < lastday:
if curr_day.day == 1:
print(curr_day)
# Instantiate a new graph containing a subset of edges: G_sub
G_sub = ____
# Add nodes from G
G_sub.____(____)
# Add in edges that fulfill the criteria
G_sub.____([(u, v, d) for u, v, d in G.edges(data=True) if d['date'] >= curr_day and d['date'] < curr_day + td])
# Get the students projection
G_student_sub = ____
# Compute the degree centrality of the students projection
dc = ____
# Append mean degree centrality to the list mean_dcs
mean_dcs.____(np.mean(____(dc.values())))
# Increment the time
curr_day += td
plt.plot(mean_dcs)
plt.xlabel('Time elapsed')
plt.ylabel('Degree centrality.')
plt.show()