Master Graph Theory Algorithms and build strong problem-solving skills. Learn essential concepts, shortest paths, trees, and networks to tackle complex challenges and enhance your computational thinking for real-world applications advanced.
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1 Year Access 
Intermediate 
3 Students 
8 hours, 34 minutes Get access to 5000+ courses including this for only £49. Learn More.
Master The Graph Theory Algorithms is a comprehensive course designed to help learners understand the principles of Graph Theory, Graph Algorithms, and advanced problem-solving techniques in Computer Science. Explore essential Data Structures, algorithmic concepts, and computational methods while developing strong foundations in graph-based thinking, network analysis, and efficient algorithm design for real-world applications.
This course covers key graph traversal techniques, including Depth First Search (DFS) and Breadth First Search (BFS), alongside advanced shortest path algorithms such as Dijkstra, Bellman-Ford, and Floyd-Warshall. Learners will explore trees, topological sorting, minimum spanning trees, and other important concepts that strengthen their understanding of algorithmic thinking and computational problem-solving.
Through practical lessons on Tarjan Algorithm, Bridge and Articulation Points, Travelling Salesman Problem (TSP), Eulerian Paths and Circuits, Prim’s Algorithm, and Network Flow, this course builds advanced skills in graph-based solutions. Ideal for aspiring developers, programmers, and computer science learners seeking to master algorithm design and improve their technical expertise.
After completing the Master The Graph Theory Algorithms course assessment, you will be eligible to receive a CPD-accredited certificate worth £9 from One Education to demonstrate your achievement.
The certificate is also available as a printed hard copy delivered by post for £15.
Master The Graph Theory Algorithms introduces learners to the fundamental concepts and advanced techniques used in graph-based problem solving. This course explores Graph Theory, Graph Algorithms, Data Structures, Algorithm Design, Network Analysis, Graph Traversal, Shortest Path Algorithms, Computational Methods, Graph Representation, and Algorithmic Thinking to build strong foundations in computer science and mathematical modelling.
Studying Master The Graph Theory Algorithms develops advanced problem-solving, analytical reasoning, and algorithm design skills. The course supports students, programmers, and computer science learners by enhancing their understanding of graph traversal methods, optimisation techniques, network structures, and practical applications of graph algorithms in technology, data science, and computational systems.
The Master The Graph Theory Algorithms course has a total study time of 8 hours, 34 minutes. This comprehensive programme is designed for flexible learning, allowing learners to progress at their own pace while developing graph theory and algorithmic skills, including graph algorithms, data structures, algorithm design, network analysis, graph traversal, shortest path algorithms, computational methods, computer science concepts, algorithmic thinking, and problem-solving techniques.
The Master The Graph Theory Algorithms course requirements are simple and suitable for learners with a basic background in mathematics and computer science. Participants should have an interest in graph theory, algorithms, data structures, network analysis, and problem-solving, and a willingness to explore graph traversal, shortest path algorithms, algorithm design, and computational methods. Access to an internet-enabled device and commitment to regular study and practice are recommended for effective learning and successful course completion.
This course focuses on graph theory concepts and algorithmic problem-solving, covering graph structures, graph algorithms, data structures, graph traversal, shortest path algorithms, network analysis, algorithm design, computational methods, and techniques used in computer science and real-world applications.
No. It is suitable for students, programmers, computer science learners, software developers, and anyone interested in improving algorithmic thinking, problem-solving skills, and understanding graph-based computational techniques.
The course is delivered online, allowing you to learn at your own pace and develop practical knowledge of graph theory, algorithm implementation, and advanced problem-solving approaches through structured lessons.
Yes. Algorithm exercises, graph-based problems, coding activities, practical examples, data structure tasks, case studies, knowledge checks, and quizzes may be included to reinforce learning.
You will receive a certificate of completion after successfully finishing the course.
This course supports career development in software engineering, computer science, data structures, artificial intelligence, machine learning, network analysis, data science, algorithm development, and technology roles requiring strong computational skills.
| Module 01: Introduction | |||
| Introduction | 00:14:00 | ||
| Module 02: Common Problem | |||
| Common Problem | 00:10:00 | ||
| Module 03: Depth First Search | |||
| Depth First Search | 00:11:00 | ||
| Module 04: Breadth First Search | |||
| Breadth First Search | 00:08:00 | ||
| Module 05: Breadth First Search Shortest Path on a Grid | |||
| Breadth First Search Shortest Path on a Grid | 00:17:00 | ||
| Module 06: Trees | |||
| Storage and Representation of Trees | 00:10:00 | ||
| Beginner Tree Algorithms | 00:10:00 | ||
| Rooting Tree | 00:05:00 | ||
| Center(s) of a Tree | 00:06:00 | ||
| Isomorphisms in Trees | 00:11:00 | ||
| Isomorphisms in Trees Source Code | 00:10:00 | ||
| Lowest Common Ancestor | 00:17:00 | ||
| Module 07: Topological Sort | |||
| Topological Sort | 00:14:00 | ||
| Shortest and Longest Paths on DAGs | 00:10:00 | ||
| Khan’s Algorithm | 00:13:00 | ||
| Module 08: Dijkstra | |||
| Dijkstra’s Shortest Path Algorithm | 00:25:00 | ||
| Dijkstra’s Shortest Path Algorithm Source Code | 00:09:00 | ||
| Module 09: Bellman-Ford Algorithm | |||
| Bellman-Ford Algorithm | 00:15:00 | ||
| Module 10: Floyd-Warshall Algorithm | |||
| Floyd-Warshall Algorithm | 00:16:00 | ||
| Floyd-Warshall Algorithm Source Code | 00:09:00 | ||
| Module 11: Bridge and Algorithm Points | |||
| Algorithm to Find Bridges and Articulation Points | 00:20:00 | ||
| Algorithm to Find Bridges and Articulation Points Source Code | 00:09:00 | ||
| Module 12: Tarjan Algorithm | |||
| Tarjan’s Algorithm for Finding Strongly Connected Components | 00:17:00 | ||
| Tarjan’s Algorithm for Finding Strongly Connected Components Source Code | 00:07:00 | ||
| Module 13: Travelling Salesman Problem (TSP) | |||
| Travelling Salesman Problem (TSP) with Dynamic Programming | 00:21:00 | ||
| Travelling Salesman Problem (TSP) with Dynamic Programming Source Code | 00:14:00 | ||
| Module 14: Eulerian Paths and Circuits | |||
| Existence of Eulerian Paths and Circuit | 00:10:00 | ||
| Finding Eulerian Paths and Circuits | 00:16:00 | ||
| Eulerian Paths Source Code | 00:08:00 | ||
| Module 15: Prim’s Minimum Spanning Tree Algorithm | |||
| Prim’s Minimum Spanning Tree Algorithm (Lazy Version) | 00:15:00 | ||
| Prim’s Minimum Spanning Tree Algorithm ( Eager Version) | 00:15:00 | ||
| Prim’s Minimum Spanning Tree Algorithm Source Code ( Eager Version) | 00:09:00 | ||
| Module 16: Network Flow | |||
| Max Flow Ford-Fulkerson Method | 00:13:00 | ||
| Max Flow Ford-Fulkerson Method Source Code | 00:17:00 | ||
| Network Flow: Unweighted Bipartite Graph Matching | 00:11:00 | ||
| Network Flow: Mice and Owls | 00:08:00 | ||
| Network Flow: Elementary Math | 00:11:00 | ||
| Network Flow: Edmond-Karp Algorithm Source Code | 00:06:00 | ||
| Network Flow: Edmond-Karp Algorithm Source Code | 00:10:00 | ||
| Network Flow: Capacity Scaling | 00:10:00 | ||
| Network Flow: Capacity Scaling Source Code | 00:06:00 | ||
| Network Flow: Dinic’s Algorithm | 00:12:00 | ||
| Network Flow: Dinic’s Algorithm Source Code | 00:09:00 | ||
| Assignment | |||
| Assignment – Graph Theory Algorithms | 00:00:00 | ||
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