Reading graphs
In this exercise, before you compute projections, you're going to practice working with one of NetworkX's disk I/O functions, read_edgelist(). read_edgelist() creates a graph from the edgelist file. The graph that you'll be working with is a bipartite graph describing the American Revolution. There are two node partitions - 'people' and 'clubs', and edges denote a person being a member of a club.
This is a part of the course
“Intermediate Network Analysis in Python”
Exercise instructions
- Import
networkxasnx. - Use
nx.read_edgelist()to read in'american-revolution.edgelist'. - In the dataset,
'clubs'do not have a.symbol in their node name. Use this information to assign nodes to'clubs'or'people'partitions. Remember the'bipartite'keyword! - Print the edges of the graph.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import networkx
import networkx as nx
# Read in the data: g
G = ____
# Assign nodes to 'clubs' or 'people' partitions
for n, d in G.nodes(data=True):
if '.' in n:
G.nodes[n]['____'] = '____'
else:
____ = '____'
# Print the edges of the graph
print(____)
This exercise is part of the course
Intermediate Network Analysis in Python
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
In this chapter, you will use a famous American Revolution dataset to dive deeper into exploration of bipartite graphs. Here, you will learn how to create the unipartite projection of a bipartite graph, a very useful method for simplifying a complex network for further analysis. Additionally, you will learn how to use matrices to manipulate and analyze graphs - with many computing routines optimized for matrices, you'll be able to analyze many large graphs quickly and efficiently!
Exercise 1: Concept of projectionExercise 2: Reading graphsExercise 3: Computing projectionExercise 4: Plot degree centrality on projectionExercise 5: Bipartite graphs as matricesExercise 6: Properties of bipartite adjacency matrices.Exercise 7: Compute adjacency matrixExercise 8: Find shared membership: TranspositionExercise 9: Representing network data with pandasExercise 10: Make nodelistExercise 11: Make edgelistWhat is DataCamp?
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