Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
SOCIAL AND TECHNOLOGICAL NETWORKS 805100715101 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals This course aims to introduce concepts of complex and social network analysis and its application to real social and technological networks.
Course Content Networks and Random Graphs, Small World and Weak Ties, Network Centrality and Applications, Community Detection and Overlapping Communities, Structure of the Web, Search and Power Laws, Network Robustness and Applications, Cascades and Behaviour Influence, Spatial Network Analysis, Epidemic Spreading, Influence and Epidemic Spreading Applications, Temporal Networks
Learning Outcomes 1) Understands to the basic concepts of social network analysis.
2) Collaborates with peers to apply these methods to a variety of social media.
3) Understands the link between qualitative and quantitative methods of social network analysis.
4) Understands how these social technologies impact society and vice versa.
5) Applies appropriate analysis techniques o reveal certain issues.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Networks and Random Graphics Lecture
Brainstorming
Project Based Learning 		Homework
2. Week Small World and Weak Bonds Lecture
Brainstorming
Project Based Learning 		Homework
3. Week Network Center and Applications Lecture
Brainstorming
Project Based Learning 		Homework
4. Week Community Perception and Conflicting Communities Lecture
Brainstorming
Project Based Learning 		Homework
5. Week Web Structure, Search and Power Laws Lecture
Brainstorming
Project Based Learning 		Homework
6. Week Network Strength and Applications, Cascades and Behavior Impact Lecture
Brainstorming
Project Based Learning 		Homework
7. Week Spatial Network Analysis Lecture
Brainstorming
Project Based Learning 		Homework
8. Week Outbreak Spread Lecture
Brainstorming
Project Based Learning 		Homework
9. Week Impact and Outbreak Spread Applications Lecture
Brainstorming
Project Based Learning 		Homework
10. Week Temporary Networks Lecture
Brainstorming
Project Based Learning 		Homework

Sources Used in This Course
Recommended Sources
Leskovec, J., Rajaraman, A. & Ullman, J. (2014). Mining of Massive Datasets. http://www.mmds.org/
"Easley, D. & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press . http://www.cs.cornell.edu/home/kleinber/networks-book/ "
Kempe, D. (2018). Structure and Dynamics of Information in Networks. http://david-kempe.com/teaching/structure-dynamics.pdf
YuJiawei, P. S. & Faloutsos, H. (2010). Link Mining: Models, Algorithms, and Applications. https://link.springer.com/book/10.1007/978-1-4419-6515-8

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5
PY3550000
PY6505000
PY9500500
PY11500050
PY14500005

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 3
Homework 14 3
Report (Including Preparation and presentation Time) 2 20
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 2
Time to prepare for Final Exam 1 50
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload