Graph Structures and Algorithms focuses on the intricate methodologies and applications of graph theory in various domains, including computer science, data analysis, and network theory. Participants will engage in project-based learning that emphasizes the practical application of theoretical concepts. The course is structured to provide a comprehensive understanding of graph algorithms, enabling learners to tackle complex problems and develop efficient solutions.
Through interactive sessions, participants will work on real-world projects that culminate in publishable results, fostering a collaborative environment that encourages knowledge sharing. The curriculum is designed to equip learners with the skills necessary to analyze and implement graph-based solutions effectively. By the end of the course, attendees will have a robust portfolio showcasing their work, enhancing their employability and professional standing in the field.
Introduction to Graph Theory: Definitions, types of graphs, and fundamental concepts
Graph Representations: Adjacency matrices, adjacency lists, and edge lists
Traversal Algorithms: Depth-first search (DFS) and breadth-first search (BFS)
Shortest Path Algorithms: Dijkstra’s algorithm and the Bellman-Ford algorithm
Minimum Spanning Trees: Prim’s and Kruskal’s algorithms
Network Flow Problems: Ford-Fulkerson method and applications
Graph Isomorphism: Techniques and algorithms for graph comparison
Centrality Measures: Betweenness, closeness, and degree centrality
Community Detection: Algorithms for identifying clusters within graphs
Final Project: Develop and present a comprehensive analysis of a graph-related problem, applying learned algorithms and techniques