Aspire     Graph Analytics     Graph Analytics Track 1: Getting Started with Graphs

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In order to really understand how graphs work, it is important to know how they are implemented. There are multiple ways to represent graphs in code and each representation has its own advantages and disadvantages.
In this course, you will implement graphs using three different representations - the adjacency matrix, the adjacency list, and the adjacency set. Learn how the adjacency matrix representation uses a square matrix to represent connections between the nodes of a graph and also edge weights. Next, explore how the adjacency list suffers from a major drawback: the same graph can have multiple representations. Finally, discover how the adjacency set representation has exactly one way in which a graph is represented.
When you are finished with this course, you will be able to create and work with your own graph structures and optimize them for different purposes.

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Objectives

Graph Data Structures: Representing Graphs Using Matrices, Lists, & Sets discover the key concepts covered in this course model graphs using a square adjacency matrix to represent nodes and edges represent graphs using an adjacency matrix in Python represent different graph types - directed, undirected, weighted, and unweighted using adjacency matrices model graphs using an adjacency list and adjacency set and compare the two representations represent graphs using an adjacency list in Python represent graphs using an adjacency set in Python summarize the key concepts covered in this course