Book Image

Network Science with Python and NetworkX Quick Start Guide

By : Edward L. Platt
Book Image

Network Science with Python and NetworkX Quick Start Guide

By: Edward L. Platt

Overview of this book

NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
Table of Contents (15 chapters)

Centralization and inequality

Networks can also be classified by how centralized they are—how much of their centrality is concentrated in one or a few nodes. Unequal distributions are more centralized. As an example, the most centralized network would be all nodes connected to a single hub node.

The following code plots histograms of the eigenvector centralities for each of the example networks:

# Function to plot a single histogram
def centrality_histogram(x, title=None):
plt.hist(x, density=True)
plt.title(title)
plt.xlabel("Centrality")
plt.ylabel("Density")

# Create a figure
plt.figure(figsize=(7.5, 2.5))
# Calculate centralities for each example and plot
plt.subplot(1, 3, 1)
centrality_histogram(
nx.eigenvector_centrality(G_karate).values(), title="Karate")
plt.subplot(1, 3, 2)
centrality_histogram(
nx.eigenvector_centrality...