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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

By : Platt
5 (3)
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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

5 (3)
By: 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)
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Adding attributes to nodes and edges

In the last chapter, I said that networks were entirely defined by the number of nodes and which nodes were connected. I lied. Kind of. Now that we're all a little older and wiser than we were in Chapter 1, What is a Network?, I can tell you the whole truth: sometimes, network nodes and edges are annotated with additional information. In the Graph class, each node and edge can have a set of attributes to store this additional information. Attributes can simply be a convenient place to store information related to the nodes and edges, or they can be used by visualizations and network algorithms.

The Graph class allows you to add any number of attributes to a node. For a G, network, each node's attributes are stored in the dict at G.nodes[v], where v is the node's ID. In the karate club example, the club members eventually split...

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