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• Bio: pasted a link to my professional bio
• Approx. Course Duration: 4 hrs
• Course Title: Network Analysis Made Simple

Abstract

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

Learning Objectives

By the end of this tutorial, you will learn how to:

1. Use the NetworkX package and the Python programming language to manipulate and visualize graphs,
2. Understand how graph algorithms work, particularly how to "think on" graphs,
3. Use linear algebra to represent graph problems and speed them up,
4. Load graph data to and from disk.

Course Outline

Part 1: Introduction (30 min)

• Networks of all kinds: biological, transportation.
• Representation of networks, NetworkX data structures
• Basic quick-and-dirty visualizations

Part 2: Hubs and Paths (40 min)

• Finding important nodes; applications
• Pathfinding algorithms and their applications
• Hands-on: implementing path-finding algorithms
• Visualize degree and betweenness centrality distributions

Part 3: Cliques, Triangles & Structures (40 min)

• Definition of cliques
• Triangles as the simplest complex clique, applications
• Using path-finding algorithms to find structures in a graph
• Open triangles as recommender systems.

Part 4: Bipartite Graphs (30 min)

• Definition of bipartite graphs, applications
• Constructing bipartite graphs in NetworkX
• Summary statistics of bipartite graphs

Part 5: Linear Algebra and Graphs (40 min)

• Graphs as matrices: adjacency and node feature matrices
• Message passing operations and how it is used in graph deep learning
• Speed vs. code readability tradeoffs when using matrix operations

Applications

Short examples of real-world data science applications/use-cases this course could be useful for

Recommender systems: Using graph structures to recommend products or professional connections.

Epidemiological analysis: Figure out the most important spreaders of a disease.

Logistics: Identify the most efficient path to move goods and services.

Background knowledge needed

If you're familiar with the Jupyter notebook/lab interface, are comfortable with Python programming (loops, functions, conditionals), and know how to make plots in matplotlib, you'll be well-prepared for the tutorial!

Other information

• Difficulty level: Intermediate
• GitHub repository: https://ericmjl.github.io/Network-Analysis-Made-Simple/index.html
• Quizzes? Yes
• Comments/feedback for the team:
  • Let's talk about quizzes - I have hands-on activities in the repository, so I'm wondering how much more we could add in the form of quizzes.