Network Analysis Made Simple
$7.99
Minimum price
$29.99
Suggested price

Network Analysis Made Simple

An introduction to network analysis and applied graph theory using Python and NetworkX

About the Book

As the accompanying book to the popular Network Analysis Made Simple series created and taught by Eric Ma and Mridul Seth at Python, SciPy, ODSC and PyData conferences, come learn:

  1. about the NetworkX API
  2. about the basics and fundamentals of graph theory
  3. how to read and write graphs using modern data formats (e.g. pandas DataFrames)
  4. an introduction to advanced topics, including bipartite graphs, how matrices and linear algebra relate to graph theory, and statistical inference on graphs
  5. through two case studies to help you apply the concepts and ideas learned throughout the book

To aid your learning journey, we also have a GitHub repository with Jupyter notebooks that you can execute locally or on Binder! You can find it here on GitHub. Pick up this book for a self-paced introduction, or as a reference after taking the tutorial, or simply purchase it because you appreciate the work we've put in over the past five years to make and refine the material, and want to support further updates as the Python data science ecosystem evolves!

  • Share this book

  • Categories

    • Graph Theory
    • Artificial Intelligence
    • Python
    • Computer Science
  • Feedback

    Email the Author(s)

About the Authors

Eric Ma
Eric Ma

As Principal Data Scientist at Moderna Eric leads the Data Science and Artificial Intelligence (Research) team to accelerate science to the speed of thought. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of discovering medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.

Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He is also on the core developer team of NetworkX and PyMC. In addition, he gives back to the community through code contributionsbloggingteaching, and writing.

His personal life motto is found in the Gospel of Luke 12:48.

Table of Contents

  • Preface
  • Learning Goals
    • Technical Takeaways
    • Intellectual Goals
  • Introduction to Graphs
    • Introduction
    • A formal definition of networks
    • Examples of Networks
    • Types of Graphs
    • Edges define the interesting part of a graph
  • The NetworkX API
    • Introduction
    • Data Model
    • Load Data
    • Understanding a graph’s basic statistics
    • Manipulating the graph
    • Coding Patterns
    • Further Reading
    • Further Exercises
    • Solution Answers
  • Graph Visualization
    • Introduction
    • Hairballs
    • Matrix Plot
    • Arc Plot
    • Circos Plot
    • Hive Plot
    • Principles of Rational Graph Viz
  • Hubs
    • Introduction
    • A Measure of Importance: “Number of Neighbors”
    • Generalizing “neighbors” to arbitrarily-sized graphs
    • Distribution of graph metrics
    • Reflections
    • Solutions
  • Paths
    • Introduction
    • Breadth-First Search
    • Visualizing Paths
    • Bottleneck nodes
    • Recap
    • Solutions
  • Structures
    • Introduction
    • Triangles
    • Triadic Closure
    • Cliques
    • Connected Components
    • Solutions
  • Graph I/O
    • Introduction
    • Graph Data as Tables
    • Dataset
    • Graph Model
    • Pickling Graphs
    • Other text formats
    • Solutions
  • Testing
    • Introduction
    • Why test?
    • What to test
    • Continuous data testing
    • Further reading
  • Bipartite Graphs
    • Introduction
    • What are bipartite graphs?
    • Dataset
    • Bipartite Graph Projections
    • Weighted Projection
    • Degree Centrality
    • Solutions
  • Linear Algebra
    • Introduction
    • Preliminaries
    • Path finding
    • Message Passing
    • Bipartite Graphs & Matrices
    • Performance: Object vs. Matrices
    • Acceleration on a GPU
  • Statistical Inference
    • Introduction
    • Statistics refresher
    • We are concerned with models of randomness
    • Hypothesis Testing
    • Stochastic graph creation models
    • Load Data
    • Inferring Graph Generating Model
    • Quantitative Model Comparison
    • Interpretation
  • Game of Thrones
    • Introduction
    • Finding the most important node i.e character in these networks.
    • Betweeness centrality
    • PageRank
    • Evolution of importance of characters over the books
    • So what’s up with Stannis Baratheon?
    • Community detection in Networks
    • Solutions
  • Airport Network
    • Introduction
    • Visualise the airports
    • Directed Graphs and PageRank
    • Importants Hubs in the Airport Network
    • How reachable is this network?
    • Can we find airline specific reachability?
    • Solutions

The Leanpub 60 Day 100% Happiness Guarantee

Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.

You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!

So, there's no reason not to click the Add to Cart button, is there?

See full terms...

Earn $8 on a $10 Purchase, and $16 on a $20 Purchase

We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.

(Yes, some authors have already earned much more than that on Leanpub.)

In fact, authors have earnedover $13 millionwriting, publishing and selling on Leanpub.

Learn more about writing on Leanpub

Free Updates. DRM Free.

If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).

Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.

Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.

Learn more about Leanpub's ebook formats and where to read them

Write and Publish on Leanpub

You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses!

Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks.

Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. (Or, if you are producing your ebook your own way, you can even upload your own PDF and/or EPUB files and then publish with one click!) It really is that easy.

Learn more about writing on Leanpub