Network Science with Python and NetworkX Quick Start Guide: Explore and visualize network data effectively
Authors: Edward L. Platt
ISBN-10: 1789955319
ISBN-13: 9781789955316
Released: 2019-04-26
Print Length 页数: 190 pages
Book Description
Manipulate and analyze network data with the power of Python and NetworkX
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.
What you will learn
Use Python and NetworkX to analyze the properties of individuals and relationships
Encode data in network nodes and edges using NetworkX
Manipulate,store,and summarize data in network nodes and edges
Visualize a network using circular,directed and shell layouts
Find out how simulating behavior on networks can give insights into real-world problems
Understand the ongoing impact of network science on society,and its ethical considerations
contents
1 What is a Network?
2 Working with Networks in NetworkX
3 From Data to Networks
4 Affiliation Networks
5 The Small Scale – Nodes and Centrality
6 The Big Picture – Describing Networks
7 In-Between – Communities
8 Social Networks and Going Viral
9 Simulation and Analysis
10 Networks in Space and Time
11 Visualization
12 Conclusion
