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Designing Graphical Causal Bayesian Networks in Python

Solution: Likeability in a Small Town

Explore how to create a social network graph using NetworkX in Python by adding nodes and assigning weighted edges to represent likeability or affinity. Learn to visualize and interpret the connections in a small town social graph.

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To solve this exercise, you should create the graph using NetworkX, then add the nodes of the graph and finally assign the weight to each of the edges:

Python 3.8
import networkx as nx



# Create an empty graph

social_graph = nx.Graph()



# Add nodes representing people in the community

social_graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7])



# Add weighted edges representing affinity/likeability between people

social_graph.add_weighted_edges_from([

    (1, 2, 9),

    (1, 3, 8),

    (1, 4, 3),

    (2, 3, 2),

    (2, 5, 10),

    (3, 6, 7),

    (3, 7, 1),

    (4, 5, 4),

    (4, 6, 5),

    (5, 7, 6),

    (6, 7, 8)

])



# This score will evaluate your network

score = sum(data['weight'] for _, _, data in social_graph.edges(data=True))



print("The total score is:" , score)
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