GRADUATE SCHOOL

M.SC. in Computer Engineering (With Thesis)

MATH 659 | Course Introduction and Application Information

Course Name
Graph Theory
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 659
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Third Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s)
Assistant(s) -
Course Objectives Definition of Disconnected structures and their applications. The aim gives the application of graph theory in computer sciences, operation research, social sciences and biomathematics. In this concept connectivity, graph coloring, trees, Euler and Hamilton paths, Cycles, Mathcing, Covering, Shortest path and network structures will be given.
Learning Outcomes The students who succeeded in this course;
  • will be able to define and analyze problems and to find solutions based on scientific methods.
  • will be able to understand basic concepts of graph theory
  • will be able to apply the graph coloring methods to the daily life problems
  • will be able to use the dynamic graphs for helath sciences
Course Description Graphs, some special graphs, connectivity, blocks, trees, linear paths, planarity, Kuratowsky theorem, coloring, cromatic numbers, five color theorem, four color theorem, petri nets.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Graph R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
2 Specific Graphs R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
3 Graph modelling and applications. R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
4 Walk, Distance, Path, Cycle and Trees R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
5 Subgraph and graph operations R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
6 Midterm
7 Graph Isomoprhism R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
8 Trees: Binary Trees R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
9 Catalan Numbers. Travelling Binary Trees. Spanning Trees. R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
10 Edge and Vertex Connectivity. Network Reliability. R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
11 MaxMin Duality and Menger’s Theorem.  Eular Path R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
12 Hamilton Paths and Cycles. Travelling Sales Man Problem R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
13 Binary operations and Graphs. R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
14 Graph coloring and applications in mathematica. R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
15 Petri Nets R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson, 2004.
16 Review of the Semester  

 

Course Notes/Textbooks J.Gross & J.Yellen, Graph Theory and its Applications, CRC Press, 1998
Suggested Readings/Materials Graph Theory: Modeling, Applications, and Algorithms, by Geir Agnarsson and Raymond Greenlaw, Pearson Prentice Hall, 2007

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
2
20
Project
1
25
Seminar / Workshop
Oral Exams
Midterm
1
20
Final Exam
1
30
Total

Weighting of Semester Activities on the Final Grade
70
Weighting of End-of-Semester Activities on the Final Grade
30
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
10
8
80
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
2
10
20
Project
1
7
7
Seminar / Workshop
0
Oral Exam
0
Midterms
1
30
30
Final Exam
1
40
40
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information.
2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.
9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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