This technology-agnostic course will teach you about many classical graph algorithms essential for a well-rounded software engineer. 

You will begin with an introduction to the running-time analysis of algorithms and the representation and structure of graphs. You’ll cover the depth-first search algorithm and its many applications, like detecting cycles, topological sorting, and identifying cut-vertices and strongly connected components. You will then cover algorithms for finding shortest paths and minimum spanning trees. You’ll also learn about the Ford-Fulkerson algorithm and its use in finding a maximum flow and minimum cut in networks. Moreover, you will adapt it to find maximum matchings and minimum vertex covers in bipartite graphs. Finally, you will learn about the Gale-Shapley algorithm for finding stable matchings.

The material in this course serves as a basis for other advanced algorithms, like parallel and distributed algorithms, and has many diverse applications in other computing disciplines.

Mastering Graph Algorithms

Suppose neighborhood surveillance is required for a network that consists of streets meeting at junctions. We’d like to install the smallest number of cameras at the junctions so that all the streets can be surveilled. Such a neighborhood can be modeled as a graph by considering the junctions as vertices and the streets as edges.

The problem then is to find the smallest set of vertices (junctions) that provide a _cover_ to all edges (streets), and this problem is known as the **minimum vertex cover problem**. 

Learn how the minimum vertex cover problem is the dual of the maximum matching problem.

Introduction

Review: Asymptotic Notation and Math Prerequisites

Working with Graphs

Depth-First Search and Applications

Prelude: Shortest Paths

Single-source Shortest Paths in Weighted Digraphs

All Pairs Shortest Paths

Minimum Spanning Tree

Flows

Matchings and Vertex Covers

Conclusion

The Dual Problem of Minimum Vertex Cover