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Designing Graphical Causal Bayesian Networks in Python

Cyclic and Acyclic Graphs

Explore how to create and analyze cyclic and acyclic graphs using Python's NetworkX library. Learn to visualize these graphs with circular, spring, and Kamada-Kawai layouts, enhancing the clarity of directed acyclic graphs in real-world scenarios such as project management.

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Cyclic graph

A cyclic graph is a graph in which all nodes are part of a single cycle, meaning that it is possible to traverse the graph by visiting each node exactly once and returning to the starting node. To create this graph in Python, we can use nx.cycle_graph(n) where n is the number of nodes.

Next, we show an example of how to create and plot a cyclic graph in Python. Circular layouts, as the name suggests, position nodes along a circle or concentric circles. They are particularly useful for visualizing graphs with a central node, regular structures, or graphs where circular symmetry can emphasize certain patterns.

Python 3.8
import networkx as nx
import matplotlib.pyplot as plt
# Create a cyclic graph with 5 nodes
G = nx.cycle_graph(5)
# Use circular_layout to position the nodes in a circle
pos = nx.circular_layout(G)
# Draw the graph using the positions from circular_layout
nx.draw(G, pos, with_labels=True, node_color='lightblue', font_weight='bold', node_size=800)
# Show the plot
plt.savefig('output/CyclicGraph')
plt.show()

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