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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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The DiGraph class – when direction matters

So far, all of the edges in this chapter have been undirected, with no difference between an edge from A to B, and an edge from B to A. But not all relationships in life are so symmetric. If an employee-boss relationship is described by an undirected edge, it suggests that the employee can fire the boss as easily as the other way around. While possibly good for workplace morale, such arrangements aren't the norm. NetworkX supports directed edges through the DiGraph (directed graph) class.

Many of the operations already described for the Graph class translate seamlessly to the DiGraph class. Iterating through nodes and edges, accessing attributes, and visualization are all exactly the same. But there are a few differences. This section will describe the most important of these differences.

This section will use another social...

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