GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
GRAPH THEORY/5051305
Course Title: GRAPH THEORY
Credits 3 ECTS 8
Semester 1 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Assoc.Prof. Mehmet ATAK
 -- WEB SITE(S) OF LECTURER(S)
  www.gazi.edu.tr/~matak
 -- EMAIL(S) OF LECTURER(S)
  matak@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
The usage and modeling of graphs in discrete mathematics.
Usage of graph as a decision support model in decision making problems.
Combined usage of graph theory and network optimization real world problems.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  Discrete mathematics
 --COURSE CONTENT
1. Week  Introduction: description, history, applications in theoretical and practical areas.
2. Week  Algorithms: basic definitions, computational complexity, pseudo codes.
3. Week  Representation of graphs on computers. Node-arc incendence and node-arc adjacency matrices.
4. Week  Trees: basic definitions. Types of trees.
5. Week  Spanning trees: Kruskal, prim and Sollin's algorithms.
6. Week  Path, tour and circuits: Eulerian tour and related problems.
7. Week  Path, tour and circuits: Hamiltonian tour and related problems.
8. Week  Maximum flow I: acyclic networks
9. Week  Maximum flow II: unidirectional networks.
10. Week  Midterm I
11. Week  Shortest path problems I
12. Week  Shortest path problems II
13. Week  Planar graphs and graph coloring
14. Week  Transportation, assignment and matching problems.
15. Week  Connectedness and distance in graphs.
16. Week  Activity graphs
 -- RECOMMENDED OR REQUIRED READING
  1. Discrete mathematics with graph theory, Edgar G. Goodaire, Michael M. Permanter; 2. Graph Theory and its applications, Jonathan L. Gross, J. Yell
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture
 -- WORK PLACEMENT(S)
  Not Applicable
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
35
 Assignment
1
5
 Exercises
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
15
3
45
 Practising Hours of Course Per Week
15
3
45
 Reading
15
3
45
 Searching in Internet and Library
12
3
36
 Designing and Applying Materials
0
 Preparing Reports
15
1
15
 Preparing Presentation
0
 Presentation
0
 Mid-Term and Studying for Mid-Term
1
3
3
 Final and Studying for Final
0
 Other
0
 TOTAL WORKLOAD: 
189
 TOTAL WORKLOAD / 25: 
7.56
 ECTS: 
8
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Improves and deepens the field knowledge at an expert level based on undergraduate proficiency.X
2Comprehends the interactions between the computer science and other related disciplines.X
3Uses expert level theoretical and practical knowledge acquired in the computer science field.X
4Creates new knowledge by integrating the computer science knowledge and the knowledge from related disciplines.X
5Defines a problem in the computer science field.X
6Analyses the problems in the computer science field by using scientific research methods.X
7Proposes solutions to the problems in the computer science field.X
8Solves problems in the computer science field.X
9Evaluates the results within the perspectives of quality processes.X
10Develops new approaches and methods by taking responsibility in complex situations in the application stages.X