From the bridges of 17th century Königsberg to the internet, network science emerged from a diverse range of fields, all seeking to quantify and study relationships of some kind. The networks in network science model relationships as edges between nodes, which can represent anything from a species of flower, to an atom in a crystal, to an individual in a society. To quantify properties of relationships, edges can be directed and/or weighted. NetworkX provides Python classes and functions to create and manipulate such networks with ease. By now, you should have a sense of the types of problems network science and NetworkX can solve. The following chapters will cover various applications of network science as well as related features of NetworkX, with examples of how they can be applied to real datasets.
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Network Science with Python and NetworkX Quick Start Guide
By :
Network Science with Python and NetworkX Quick Start Guide
By:
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)
Preface
Free Chapter
What is a Network?
Working with Networks in NetworkX
From Data to Networks
Affiliation Networks
The Small Scale - Nodes and Centrality
The Big Picture - Describing Networks
In-Between - Communities
Social Networks and Going Viral
Simulation and Analysis
Networks in Space and Time
Visualization
Conclusion
Other Books You May Enjoy
Customer Reviews
