Network Analysis Made Simple
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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!

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  • Categories

    • Graph Theory
    • Artificial Intelligence
    • Python
    • Computer Science
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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

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