Finding important collaborators
Almost there! You'll now look at important nodes once more. Here, you'll make use of the degree_centrality() and betweenness_centrality() functions in NetworkX to compute each of the respective centrality scores, and then use that information to find the "important nodes". In other words, your job in this exercise is to find the user(s) that have collaborated with the most number of users.
This exercise is part of the course
Introduction to Network Analysis in Python
Exercise instructions
- Compute the degree centralities of
G. Store the result asdeg_cent. - Compute the maximum degree centrality. Since
deg_centis a dictionary, you'll have to use the.values()method to get a list of its values before computing the maximum degree centrality withmax(). - Identify the most prolific collaborators using a list comprehension:
- Iterate over the degree centrality dictionary
deg_centthat was computed earlier using its.items()method. What condition should be satisfied if you are seeking to find user(s) that have collaborated with the most number of users? Hint: It has do to with the maximum degree centrality.
- Iterate over the degree centrality dictionary
- Hit 'Submit Answer' to see who the most prolific collaborator(s) is/are!
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the degree centralities of G: deg_cent
deg_cent = ____
# Compute the maximum degree centrality: max_dc
max_dc = ____
# Find the user(s) that have collaborated the most: prolific_collaborators
prolific_collaborators = [n for n, dc in ____ if ____ == ____]
# Print the most prolific collaborator(s)
print(prolific_collaborators)