Graph theory algorithms visualized
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Peers Updated12 hours ago (2026-03-24 05:17:02)
Description
Description
This Graph theory algorithms will teach students the fundamental concepts and algorithms of graph theory with real life examples and eye-appealing visualizations. The course will cover topics such as graph representation, graph traversal, topological sort, shortest paths, minimum spanning trees, graph coloring… With a total of more than 20 covered algorithms.
Discussed algorithms will be implemented in detail by using a programming language to give a better understanding for students. Captions, practice problems, quizzes, slides, and source code will also be here to make the learning experience way better.
By the end of the course, students will have a strong understanding of graph algorithms and be able to apply their knowledge to solve problems in computer science, mathematics, and beyond.
This course is ideal for students who are looking to pursue careers in computer science, mathematics, or related fields, as well as for professionals who want to expand their knowledge of graph theory algorithms.
Covered algorithms:
Graph traversal:
Depth-first search
Breadth-first search
Topological sorting:
Depth-first search based topological sort
Breadth-first search based topological sort (Kahn’s algorithm)
Shortest path:
Dijkstra’s algorithm
Bellman-Ford algorithm
Floyd-Warshall algorithm
Johnson’s algorithm
Shortest path for unweighted graphs algorithm
Shortest path for directed acyclic graphs (1st approach) algorithm
Shortest path for directed acyclic graphs (2nd approach) algorithm
Trees and minimum spanning trees:
Spanning tree algorithm
Graph to out-tree algorithm
Prim’s algorithm
Kruskal’s algorithm
Eulerian/Hamiltonian paths and cycles:
Hierholzer’s algorithm
Hamiltonian cycle backtracking algorithm
Graph coloring:
2-colorability algorithm
k-colorability backtracking algorithm
Greedy coloring algorithm
Welsh-Powell heuristic
DSatur heuristic
Who this course is for:
Computer science students
Data science beginners
Software development beginners
Requirements
Basic programming knowledge
Algorithmic techniques knowledge is preferred (recursion, backtracking, dynamic programming…)
Data structures knowledge is preferred (hash table, queue, stack, set, heap…)
Last Updated 2/2023
This Graph theory algorithms will teach students the fundamental concepts and algorithms of graph theory with real life examples and eye-appealing visualizations. The course will cover topics such as graph representation, graph traversal, topological sort, shortest paths, minimum spanning trees, graph coloring… With a total of more than 20 covered algorithms.
Discussed algorithms will be implemented in detail by using a programming language to give a better understanding for students. Captions, practice problems, quizzes, slides, and source code will also be here to make the learning experience way better.
By the end of the course, students will have a strong understanding of graph algorithms and be able to apply their knowledge to solve problems in computer science, mathematics, and beyond.
This course is ideal for students who are looking to pursue careers in computer science, mathematics, or related fields, as well as for professionals who want to expand their knowledge of graph theory algorithms.
Covered algorithms:
Graph traversal:
Depth-first search
Breadth-first search
Topological sorting:
Depth-first search based topological sort
Breadth-first search based topological sort (Kahn’s algorithm)
Shortest path:
Dijkstra’s algorithm
Bellman-Ford algorithm
Floyd-Warshall algorithm
Johnson’s algorithm
Shortest path for unweighted graphs algorithm
Shortest path for directed acyclic graphs (1st approach) algorithm
Shortest path for directed acyclic graphs (2nd approach) algorithm
Trees and minimum spanning trees:
Spanning tree algorithm
Graph to out-tree algorithm
Prim’s algorithm
Kruskal’s algorithm
Eulerian/Hamiltonian paths and cycles:
Hierholzer’s algorithm
Hamiltonian cycle backtracking algorithm
Graph coloring:
2-colorability algorithm
k-colorability backtracking algorithm
Greedy coloring algorithm
Welsh-Powell heuristic
DSatur heuristic
Who this course is for:
Computer science students
Data science beginners
Software development beginners
Requirements
Basic programming knowledge
Algorithmic techniques knowledge is preferred (recursion, backtracking, dynamic programming…)
Data structures knowledge is preferred (hash table, queue, stack, set, heap…)
Last Updated 2/2023