Blended live training with Eric J. Ma starts on September 30th at 1 PM (ET)
Training duration: 4 hours (Handson)
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Learning Objectives

Use the NetworkX package and the Python programming language to manipulate and visualize graphs

Understand how graph algorithms work, particularly how to "think on" graphs

Use linear algebra to represent graph problems and speed them up

Load graph data to and from disk
Instructor Bio:
Eric Ma
Principal Data Scientist, Platform Research  Moderna Therapeutics
Eric J. Ma
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DIFFICULTY LEVEL: INTERMEDIATE
Course Abstract
Course Outline
Part 1: Introduction
 Networks of all kinds: biological, transportation.
 Representation of networks, NetworkX data structures
 Basic quickanddirty visualizations
Part 2: Hubs and Paths
 Finding important nodes; applications
 Pathfinding algorithms and their applications
 Handson: implementing pathfinding algorithms
 Visualize degree and betweenness centrality distributions
Part 3: Cliques, Triangles & Structures
 Definition of cliques
 Triangles as the simplest complex clique, applications
 Using pathfinding algorithms to find structures in a graph
 Open triangles as recommender systems.
Part 4: Bipartite Graphs
 Definition of bipartite graphs, applications
 Constructing bipartite graphs in NetworkX
 Summary statistics of bipartite graphs
Part 5: Linear Algebra and Graphs
 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
Which knowledge and skills you should have?

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 wellprepared for the tutorial!
What is included in your ticket?

Access to ondemand session and live exercises and discussion with the instructor

Access to the ondemand recording

Certificate of completion
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