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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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Reading and writing network files

NetworkX provides support for reading and writing many network file formats. Of course, if a network has been provided in one of these formats, it will be very easy to load into NetworkX! But, even if you have data in another format, it is often possible to convert it to one of the supported formats without too much difficulty (I would guess that 90% of network science work is converting data between formats most of the rest is complaining about converting data). Spreadsheets, for instance, can often be converted to an appropriate format just by reordering columns and exporting as tab-separated values (TSV format). This section will describe several common formats, including adjacency list, edge list, GEXF, and JSON.

The edge list format is a simple but useful plain-text format. It supports edge attributes, but not node attributes. Edge lists...

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