Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3VTK230Social Network Analysis3+0+03323.08.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Associate Degree
Department / Program Web Design and Development
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course This course aims to teach the fundamental concepts and techniques for examining the structure of social networks and the relationships within these networks using quantitative methods. Students gain skills in modeling network data, calculating basic network measures, visualizing networks, and interpreting the obtained results.
Course Content Social network analysis concepts, nodes and links, directed and undirected networks, weighted networks, adjacency matrices and edge lists, degree and density, centrality measures, connectivity and components, clustering, community structures, network visualization, social media networks, basic network data collection, analysis, and interpretation applications.
Course Methods and Techniques Lecturing, practice, case studies, problem solving, data analysis, and project-based learning.
Prerequisites and co-requisities None
Course Coordinator Instructor Afife ÇİFTCİ OLGUN
Name of Lecturers Instructor AFİFE ÇİFTCİ OLGUN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Wasserman, S. & Faust, K. Social Network Analysis: Methods and Applications. Cambridge University Press.
Newman, M. Networks. Oxford University Press.
NetworkX documentation and course application notes.
Course Notes Lecture presentations, application examples, and NetworkX application notes.

Course Category
Field %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 2 % 10
Project 1 % 20
Final examination 1 % 40
Total
5
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 1 14
Assignments 2 4 8
Mid-terms 1 5 5
Project 1 8 8
Final examination 1 13 13
Total Work Load   Number of ECTS Credits 3 90

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Bilgi 
1 Explains the fundamental concepts of social network analysis, network types, and network data structures.
Beceri 
2 Creates and organizes network data in node-link, edge list, and matrix formats.
3 Calculates and interprets basic network measures such as degree, density, centrality, connectivity, and clustering.
4 Visualizes social network data using appropriate tools and analyzes patterns in the network structure.
Yetkinlik 
5 Performs analysis on a real or sample social network dataset, evaluates the results, and reports them.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Concept of social network analysis, application areas, and basic terminology
2 Introduction to graph theory: node, edge, adjacency, and path concepts
3 Directed, undirected, weighted, and bipartite networks
4 Representation of network data: edge list, adjacency list, and adjacency matrix
5 Network size, degree distribution, and network density
6 Connectivity, paths, distance, diameter, and components
7 Centrality measures: degree and closeness centrality
8 Midterm Exam
9 Betweenness and eigenvector centrality; interpretation of centrality results
10 Clustering coefficient, triangles, and local network structures
11 Community concept and basic community detection approaches
12 Network visualization principles and layout methods
13 Creating networks, calculating basic measures, and visualization with NetworkX
14 Analysis of social media and online interaction networks; data ethics and privacy
15 Integrated analysis, interpretation, and project work on a sample social network dataset
16 Final Exam
17 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16
In1 4
Sk2 4 3 3
Sk3 4 4 4 3
Sk4 4 5 4 3
Co5 4 4 5 4 4 4 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant