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)

Affiliation networks in NetworkX

Affiliation networks are represented in NetworkX using the same classes as other networks: Graph and DiGraph. The only difference is that you need to keep track of which nodes are of which type. In NetworkX, this is done by using a container (list, set, and so on) to store all node IDs for one node type. For convenience, the node type can also be stored as a node attribute. A network can be tested for an affiliation network structure using the sets() function. Usually, this function can also find the nodes of each type (the exception being when the network has groups of nodes that are not connected to the rest of the network). The following example loads our dear old friend from Chapter 2, Working with Networks in NetworkX, the Zachary karate club network, and checks it for affiliation network structure:

# Import bipartite module
from networkx.algorithms...