Developing with Graph Algorithms

Graphs are powerful data structures that we can use to model real-world relationships of all kinds. Through the paradigm of vertices (or nodes) that represent data, and edges (the connections between vertices), graphs can represent highly complex interconnections in nearly any environment, and you can see them in practical use in everything from social media apps (e.g., Facebook and LinkedIn) to the GPS apps in your phone and car. For each specific use, we can use algorithms that determine and direct how we use a graph, including, for example, algorithms that help networking systems determine the shortest path by which to send packet data to a destination, or those that make suggestions for new friends in your favorite social media app.

In this video course, designed for beginner- to intermediate-level developers and data scientists, host Mark Needham introduces graph algorithms and demonstrates how you can incorporate them into your software development and data science workflow. Your exploration begins by learning about three different categories of algorithms, including within them the world-famous PageRank algorithm, and going through some use cases that are particularly well suited for graph algorithms. You’ll see how to install Neo4j and the graph algorithms library as well as how you can use graph algorithms with Python in a Jupyter notebook. Later, Mark takes you through worked examples using each of the algorithms on real-world datasets. You’ll even get to apply your knowledge of graph algorithms by working through an end-to-end example on a Game of Thrones dataset, also involving graph visualization.