
Master degree centrality, the simplest centrality measure, by calculating node degrees from adjacency matrices. Distinguish in-degree and out-degree in directed graphs and view degree centrality as descriptive statistics.
Explore betweenness centrality as a gatekeeper metric in graphs, using shortest paths and counts of paths that go through a node to assess how removing a node slows information spread.
Apply union find to build a minimum spanning tree by placing each node in its own set, using union operations as edges are processed in increasing weight order.
What is a graph?
A Graph is a collection of Nodes and Edges. The nodes represent entities, such as people, computers in a network, or molecules in a chemical reaction. The edges represent the relationships between them such as friendships (or frenemies), direct connections, or constituents in a reaction.
Graph databases are online systems that let people manage graph data. Unlike older databases, priority is given to relationships between entities. This means you don’t have to mess around with complicated keys and joins to analyze large portions of a system.
Why are graph databases important?
Graphs are growing in prevalence. Every time you visit Facebook, you’re getting information on first, second, and even third-degree connections to you and your friends.
The biggest tech companies around leverage graph data and analytics to understand how users relate to each other, and with the content on their site.
What does this course teach?
This course will provide an intuition-first approach to understanding, analyzing, and manipulating graph data.
I’ve picked out only the most important algorithms, and build solutions from the ground up using real world examples
Is this course right for me?
This course is intended for students who want to prepare for the workforce, professionals who want to learn more about graph data and keep abreast of new technology, and anyone with a curios mind and desire to learn.