Book Image

Network Science with Python

By : David Knickerbocker
Book Image

Network Science with Python

By: David Knickerbocker

Overview of this book

Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You’ll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You’ll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you’ll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You’ll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you’ll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1: Getting Started with Natural Language Processing and Networks
Part 1: Getting Started with Natural Language Processing and Networks
Free Chapter
2
Chapter 1: Introducing Natural Language Processing
Chapter 1: Introducing Natural Language Processing
Technical requirements
What is NLP?
Why NLP in a network analysis book?
A very brief history of NLP
How has NLP helped me?
Common uses for NLP
Advanced uses of NLP
How can a beginner get started with NLP?
Summary
3
Chapter 2: Network Analysis
Chapter 2: Network Analysis
The confusion behind networks
What is this network stuff?
Resources for learning about network analysis
Common network use cases
Advanced network use cases
Getting started with networks
Summary
Further reading
4
Chapter 3: Useful Python Libraries
Chapter 3: Useful Python Libraries
Technical requirements
Using notebooks
Data analysis and processing
Data visualization
NLP
Network analysis and visualization
NetworkX
ML
Summary
5
Part 2: Graph Construction and Cleanup
Part 2: Graph Construction and Cleanup
6
Chapter 4: NLP and Network Synergy
Chapter 4: NLP and Network Synergy
Technical requirements
Why are we learning about NLP in a network book?
Asking questions to tell a story
Introducing web scraping
Choosing between libraries, APIs, and source data
Using NLTK for PoS tagging
Using spaCy for PoS tagging and NER
Converting entity lists into network data
Converting network data into networks
Doing a network visualization spot check
Additional NLP and network considerations
Summary
7
Chapter 5: Even Easier Scraping!
Chapter 5: Even Easier Scraping!
Technical requirements
Why cover Requests and BeautifulSoup?
Getting started with Newspaper3k
Introducing the Twitter Python Library
Summary
8
Chapter 6: Graph Construction and Cleaning
Chapter 6: Graph Construction and Cleaning
Technical requirements
Creating a graph from an edge list
Listing nodes
Removing nodes
Quick visual inspection
Adding nodes
Renaming nodes
Removing edges
Persisting the network
Simulating an attack
Summary
9
Part 3: Network Science and Social Network Analysis
Part 3: Network Science and Social Network Analysis
10
Chapter 7: Whole Network Analysis
Chapter 7: Whole Network Analysis
Technical requirements
Creating baseline WNA questions
WNA in action
Comparing centralities
Visualizing subgraphs
Investigating islands and continents – connected components
Understanding layers with k_core and k_corona
Challenge yourself!
Summary
11
Chapter 8: Egocentric Network Analysis
Chapter 8: Egocentric Network Analysis
Technical requirements
Egocentric network analysis
Investigating ego nodes and connections
Identifying other research opportunities
Summary
12
Chapter 9: Community Detection
Chapter 9: Community Detection
Technical requirements
Introducing community detection
Getting started with community detection
Exploring connected components
Using the Louvain method
Using label propagation
Using the Girvan-Newman algorithm
Other approaches to community detection
Summary
13
Chapter 10: Supervised Machine Learning on Network Data
Chapter 10: Supervised Machine Learning on Network Data
Technical requirements
Introducing ML
Beginning with ML
Data preparation and feature engineering
Selecting a model
Preparing the data
Training and validating the model
Model insights
Other use cases
Summary
14
Chapter 11: Unsupervised Machine Learning on Network Data
Chapter 11: Unsupervised Machine Learning on Network Data
Technical requirements
What is unsupervised ML?
Introducing Karate Club
Network science options
Uses of unsupervised ML on network data
Constructing a graph
Community detection in action
Graph embeddings in action
Using embeddings in supervised ML
Summary
15
Index
Index
Why subscribe?
16
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Introducing Natural Language Processing

Why in the world would a network analysis book start with Natural Language Processing (NLP)?! I expect you to be asking yourself that question, and it’s a very good question. Here is why: we humans use language and text to describe the world around us. We write about the people we know, the things we do, the places we visit, and so on. Text can be used to reveal relationships that exist. The relationship between things can be shown via network visualization. It can be studied with network analysis.

In short, text can be used to extract interesting relationships, and networks can be used to study those relationships much further. We will use text and NLP to identify relationships and network analysis and visualization to learn more.

NLP is very useful for creating network data, and we can use that network data to learn network analysis. This book is an opportunity to learn a bit about NLP and network analysis, and how they can be used together.

In explaining NLP at a very high level, we will be discussing the following topics:

  • What is NLP?
  • Why NLP in a network analysis book?
  • A very brief history of NLP
  • How has NLP helped me?
  • Common uses for NLP
  • Advanced uses of NLP
  • How can a beginner get started with NLP?
Previous Section
End of Section 1
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