How to draw node colormap in NetworkX/Matplotlib?

To draw a node colormap in NetworkX with Matplotlib, you can assign different colors to nodes based on numerical values and specify a colormap. This creates visually appealing graphs where node colors represent data values.

Steps to Create Node Colormap

• Set the figure size and adjust the padding between and around the subplots
• Create a graph structure (cycle graph with cyclically connected nodes)
• Position the nodes using a layout algorithm
• Draw the graph with node colors mapped to a colormap
• Display the figure using show() method

Basic Example

Here's how to create a circular graph with nodes colored using a colormap −

import matplotlib.pyplot as plt
import networkx as nx

plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create a cycle graph with 24 nodes
G = nx.cycle_graph(24)

# Position nodes in a circle
pos = nx.circular_layout(G)

# Draw with node colormap
nx.draw(G, pos, node_color=range(24), node_size=800, cmap='copper')

plt.show()

Advanced Example with Custom Colors

You can also use custom color values and different colormaps −

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

# Create a random graph
G = nx.erdos_renyi_graph(15, 0.3)

# Generate random color values for nodes
node_colors = np.random.rand(len(G.nodes()))

# Position nodes using spring layout
pos = nx.spring_layout(G, seed=42)

# Draw with viridis colormap
nx.draw(G, pos, 
        node_color=node_colors, 
        node_size=600, 
        cmap='viridis',
        with_labels=True)

plt.colorbar(plt.cm.ScalarMappable(cmap='viridis'))
plt.title("NetworkX Graph with Node Colormap")
plt.show()

Key Parameters

Output

The code generates a circular graph where each node is colored according to its position in the sequence, creating a gradient effect using the copper colormap.

Conclusion

Using node_color with a cmap parameter allows you to create visually informative graphs where node colors represent data values. This technique is particularly useful for visualizing network properties or clustering results.