SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
60613METOS-YZM0197	Applications of Graph Theory	0	Spring	2	2	3	5

Course Type :	Elective Course I
Cycle:	Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Dr. Öğr. Üyesi GİZEM TEMELCAN ERGENECOŞAR
Dersin Öğretim Eleman(lar)ı:
Dersin Kategorisi:

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:	The objective of this course is to teach the general concepts of graph theory, to provide the ability to model real-world problems using graph structures and to solve these problems through graph algorithms.
Course Content:	Basic concepts related to graphs, types of graphs, isomorphism, matrix representations of graphs, modeling with graphs, coloring problem in graphs, graph traversal algorithms, shortest path algorithms, minimum spanning tree algorithms, applications of graphs.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) Understand and define key concepts in graph theory (vertices, edges, directed/undirected graphs).

2) Explain the principles and workings of essential graph algorithms (DFS, BFS, Dijkstra's, Prim's, Kruskal's).

3) Identify and describe the various application areas of graph theory,

Skills (Describe as Cognitive and/or Practical Skills.)

1) Effectively model real-world problems using graph structures and representations.

2) Apply graph algorithms to solve specific problems, demonstrating proficiency in implementation.

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Basic fundamentals of graphs, importance of graph theory
2)	Definitions and terminology (vertices, edges, directed/undirected)
3)	Types of Graphs
4)	Graphs and isomorphism, matrix representations
5)	Graph Traversal Algorithms (Depth-First Search (DFS),Breadth-First Search (BFS)) Applications of traversal algorithms
6)	Shortest Path Algorithms: Dijkstra’s algorithm, Bellman-Ford algorithm, applications
7)	Graph Modelling Applications
8)	Midterm
9)	Minimum Spanning Trees: Prim’s and Kruskal’s algorithms
10)	Graph coloring concepts and applications
11)	Real world applications of graph theory
12)	Markov Models
13)	Graph algorithms in programming
14)	Review

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:	"Discrete and Combinatorial Mathematics: An Applied Introduction" by R. P. Grimaldi , Pearson, 5th Edition, 2003.
References:	"Graph Theory: Modeling, Applications, and Algorithms" by Geir Agnarsson and Raymond Greenlaw, Pearson, 1st Edition, 2007.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	5	3	4
Program Öğrenme Çıktıları (PÖÇ)
1) Knowledge in mathematics, natural sciences, basic engineering, and software engineering–specific subjects; and the ability to use this knowledge in solving complex engineering problems.
2) Ability to identify, formulate, and analyze complex engineering problems by applying knowledge of basic sciences, mathematics, and engineering, while taking into account the relevant UN Sustainable Development Goals.
3) Ability to design creative solutions to complex engineering problems; ability to design complex systems, processes, devices, or products in a way that meets present and future needs, while considering realistic constraints and conditions.
4) Ability to select and use appropriate techniques, resources, and modern engineering and informatics tools—including prediction and modeling—for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5) Ability to use research methods—including literature review, experimental design, experimentation, data collection, analysis, and interpretation of results—for the investigation of complex engineering problems.
6) Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7) Knowledge of ethical responsibility and conduct in accordance with the principles of the engineering profession; awareness of acting impartially, without discrimination, and embracing diversity.
8) Ability to work effectively, individually and as a member or leader of intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid).
9) Ability to communicate effectively on technical subjects, orally and in writing, by taking into account the diverse characteristics of the target audience (such as education, language, and profession).
10) Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11) An ability to engage in lifelong learning, including independent and continuous learning, to adapt to new and emerging technologies, and to critically evaluate technological changes.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Knowledge in mathematics, natural sciences, basic engineering, and software engineering–specific subjects; and the ability to use this knowledge in solving complex engineering problems.
2)	Ability to identify, formulate, and analyze complex engineering problems by applying knowledge of basic sciences, mathematics, and engineering, while taking into account the relevant UN Sustainable Development Goals.
3)	Ability to design creative solutions to complex engineering problems; ability to design complex systems, processes, devices, or products in a way that meets present and future needs, while considering realistic constraints and conditions.
4)	Ability to select and use appropriate techniques, resources, and modern engineering and informatics tools—including prediction and modeling—for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5)	Ability to use research methods—including literature review, experimental design, experimentation, data collection, analysis, and interpretation of results—for the investigation of complex engineering problems.
6)	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7)	Knowledge of ethical responsibility and conduct in accordance with the principles of the engineering profession; awareness of acting impartially, without discrimination, and embracing diversity.
8)	Ability to work effectively, individually and as a member or leader of intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid).
9)	Ability to communicate effectively on technical subjects, orally and in writing, by taking into account the diverse characteristics of the target audience (such as education, language, and profession).
10)	Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11)	An ability to engage in lifelong learning, including independent and continuous learning, to adapt to new and emerging technologies, and to critically evaluate technological changes.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Quizzes	1	% 10.00
Project	1	% 20.00
Midterms	1	% 20.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	2	28
Laboratory	0	0	0
Application	14	2	28
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	14	2	28
Presentations / Seminar	0	0	0
Project	1	18	18
Homework Assignments	2	3	6
Total Workload of Teaching & Learning Activities	-	-	108
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	1	4	4
Midterms	1	8	8
Semester Final Exam	1	12	12
Total Workload of Assesment & Evaluation Activities	-	-	24
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			132
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			5