Compute number of neighbors for each node
How do you evaluate whether a node is an important one or not? There are a few ways to do so, and here, you're going to look at one metric: the number of neighbors that a node has.
Every NetworkX graph G exposes a .neighbors(n) method that returns an iterator of nodes that are the neighbors of the node n. To begin, use this method in the IPython Shell on the Twitter network T to get the neighbors of of node 1. This will get you familiar with how the function works. Then, your job in this exercise is to write a function that returns all nodes that have m neighbors.
This is a part of the course
“Introduction to Network Analysis in Python”
Exercise instructions
- Write a function called
nodes_with_m_nbrs()that has two parameters -Gandm- and returns all nodes that havemneighbors. To do this:- Iterate over all nodes in
G(not including the metadata). - Use the
len()andlist()functions together with the.neighbors()method to calculate the total number of neighbors that nodenin graphGhas.- If the number of neighbors of node
nis equal tom, addnto the setnodesusing the.add()method.
- If the number of neighbors of node
- After iterating over all the nodes in
G, return the setnodes.
- Iterate over all nodes in
- Use your
nodes_with_m_nbrs()function to retrieve all the nodes that have 6 neighbors in the graphT.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define nodes_with_m_nbrs()
def ____:
"""
Returns all nodes in graph G that have m neighbors.
"""
nodes = set()
# Iterate over all nodes in G
for n in ____:
# Check if the number of neighbors of n matches m
if ____ == ____:
# Add the node n to the set
____
# Return the nodes with m neighbors
return nodes
# Compute and print all nodes in T that have 6 neighbors
six_nbrs = ____
print(____)This exercise is part of the course
Introduction to Network Analysis in Python
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
You'll learn about ways to identify nodes that are important in a network. In doing so, you'll be introduced to more advanced concepts in network analysis as well as the basics of path-finding algorithms. The chapter concludes with a deep dive into the Twitter network dataset which will reinforce the concepts you've learned, such as degree centrality and betweenness centrality.
Exercise 1: Degree centralityExercise 2: Compute number of neighbors for each nodeExercise 3: Compute degree distributionExercise 4: Degree centrality distributionExercise 5: Graph algorithmsExercise 6: Shortest Path IExercise 7: Shortest Path IIExercise 8: Shortest Path IIIExercise 9: Betweenness centralityExercise 10: NetworkX betweenness centrality on a social networkExercise 11: Deep dive - Twitter networkExercise 12: Deep dive - Twitter network part IIWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.