Graph Theory Algorithms Course in Computer Science
A complete overview of graph theory algorithms in computer science and mathematics
Updated on Jun, 2025
Language - English
Development ,Software Engineering,Algorithms
Lectures -33
Duration -6.5 hours
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Course Description
This course provides a complete introduction to Graph Theory algorithms in computer science.
Topics covered in these videos include: how to store and represent graphs on a computer; common graph theory problems seen in the wild; famous graph traversal algorithms (DFS & BFS); Dijkstra's shortest path algorithm (both the lazy and eager version); what a topological sort is, how to find one, and places it's used; learning about detecting negative cycles and finding shortest paths with the Bellman-Ford and Floyd-Warshall algorithms; discovering bridges and articulation points in graphs; understanding and detecting strongly connected components with Tarjan's algorithm, and finally solving the travelling salesman problem with dynamic programming.
Who this course is for?
- Anybody ready for a deep dive into graph theory.
Goals
- Storage and representation of graphs (networks) on a computer.
- Common graph theory problems.
- Breadth-first search algorithm.
- Depth-first search algorithm.
- Dijkstra's algorithm.
- Topological sort algorithm.
- Shortest/longest path on an acyclic graph.
- Bellman Ford's algorithm.
- Floyd-Warshall all pairs shortest path algorithm.
- Finding bridges/articulation points.
- Finding strongly connected components (Tarjan's).
- Travelling salesman problem (TSP).
Prerequisites
- Exposure to computer science fundamentals (for example - data structures, recursion, data types, classes, OOP).
- Some prior programming knowledge.
Curriculum
Check out the detailed breakdown of what’s inside the course
Graph Theory Algorithms
32 Lectures
-
Graph Theory Introduction
14:24
14:24
-
Problems in Graph Theory
09:59
09:59
-
Depth First Search algorithm
10:39
10:39
-
Breadth First Search algorithm
07:45
07:45
-
Breadth First Search grid shortest path
16:50
16:50
-
Dijkstra's shortest path algorithm
24:31
24:31
-
Dijkstra's shortest path algorithm | source code
09:11
09:11
-
Topological sort algorithm
14:04
14:04
-
Shortest/longest path on a Directed Acyclic Graph (DAG)
10:14
10:14
-
Bellman-Ford algorithm
15:16
15:16
-
Floyd-Warshall all pairs shortest path algorithm
15:55
15:55
-
Floyd-Warshall all pairs shortest path algorithm | source code
09:28
09:28
-
Bridges & Articulation points
20:16
20:16
-
Bridges & Articulation points | source code
09:22
09:22
-
Tarjan's strongly connected components algorithm
17:00
17:00
-
Tarjan's strongly connected components algorithm | source code
07:11
07:11
-
Travelling Salesman problem
20:48
20:48
-
Travelling Salesman problem | source code
13:32
13:32
-
Existence of Eulerian path and circuits
09:40
09:40
-
Eulerian path algorithm
15:34
15:34
-
Eulerian path source code
08:17
08:17
-
Max Flow Ford Fulkerson | Network Flow
13:05
13:05
-
Max Flow Ford Fulkerson | source code
17:28
17:28
-
Unweighted bipartite matching | Network flow
11:21
11:21
-
Mice and Owls | Network Flow
08:27
08:27
-
Elementary Math | Network Flow
10:44
10:44
-
Edmonds Karp | Network Flow
09:31
09:31
-
Edmonds Karp | Network Flow | Source Code
05:47
05:47
-
Capacity Scaling | Network Flow
10:09
10:09
-
Capacity Scaling | Network Flow | Source Code
06:23
06:23
-
Dinic's Algorithm | Network Flow
11:39
11:39
-
Dinic's Algorithm | Network Flow | Source Code
09:26
09:26
Instructor Details
William Fiset
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