Graph Algorithms

• 39 hrs lectures
• 13 hrs projects

• 60 pts final exam (written part)
• 15 pts mid-term test (written part)
• 25 pts projects

Familiarity with graphs and graph algorithms with their complexities.
Fundamental ability to construct an algorithm for a graph problem and to analyze its time and space complexity.

First, we recall all important algorithms for systematic graph exploration including the demonstrations of algorithm correctness. Then, we proceed to more demanding algorithms for shortest path search and other advanced graph analysis. We place emphasis on the explanation of the algorithm principles and implementation discussion including the discussion of used data structures and their time/space complexities. Apart from the graph algorithms, the student improves his/her ability to formally describe an algorithm and estimate its complexity. In project, the students are usually asked to modify, implement and experiment with some chosen graph algorithm(s).

Foundations in discrete mathematics and algorithmic thinking.

• T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, 3rd edition. MIT Press, 2009.
• J.A. McHugh, Algorithmic Graph Theory, Prentice-Hall, 1990.
• K. Erciyes: Guide to Graph Algorithms (Sequential, Parallel and Discributed). Springer, 2018.

• A. Mitina: Applied Combinatorics with Graph Theory. NEIU, 2019.

1. Introduction, algorithmic complexity, basic notions and graph representations.
2. Graph searching, depth-first search, breadth-first search.
3. Topological sort, acyclic graphs.
4. Graph components, strongly connected components, examples.
5. Trees, minimal spanning trees, algorithms of Jarník and Borůvka.
6. Growing a minimal spanning tree, algorithms of Kruskal and Prim.
7. Single-source shortest paths, the Bellman-Ford algorithm, shortest path in DAGs.
8. Dijkstra's algorithm. All-pairs shortest paths.
9. Shortest paths and matrix multiplication, the Floyd-Warshall algorithm.
10. Flows and cuts in networks, maximal flow, minimal cut, the Ford-Fulkerson algorithm.
11. Matching in bipartite graphs, maximal matching.
12. Graph coloring, Chromatic polynomial.
13. Eulerian graphs and tours, Chinese postman problem, and Hamiltonian cycles.

1. Solving of selected graph problems and presentation of solutions (principle, complexity, implementation, optimization).

• Mid-term written examination (15 point)
• Evaluated project(s) (25 points)
• Final written examination (60 points)
• The minimal number of points which can be obtained from the final exam is 25. Otherwise, no points will be assigned to a student.

In case of illness or another serious obstacle, the student should inform the faculty about that and subsequently provide the evidence of such an obstacle. Then, it can be taken into account within evaluation:

• The student can ask the responsible teacher to extend the time for the project assignment.
• If a student cannot attend the mid-term exam, (s)he can ask to derive points from the evaluation of his/her first attempt of the final exam.