Master of Science in Computer Engineering

Complex Networks Analysis(INF 514)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
INF 514 Complex Networks Analysis 2 3 0 0 3 6
Prerequisites
Admission Requirements
Language of Instruction English
Course Type Elective
Course Level Masters Degree
Course Instructor(s) Günce Keziban ORMAN korman@gsu.edu.tr (Email)
Assistant
Objective In this class, we will describe both theoretical and practical aspects of complex networks analysis. We will review some basic graph theoretical concepts allowing to define the main properties observed in real-world complex networks (small-world effect, scale-free networks, preferential attachment, etc.). We will also describe the principal models allowing to randomly generate networks. We will present the main methods and tools used to analyze and interpret networks (community detection, link prediction, information propagation, resilience to attacks...). As an illustration, we will apply them to some real-world data (Internet, social networks, etc.)
Content 1. Introduction
2. Basic graph theoretical notions
3. Random graphs and models I
4. Random graphs and models II
5. Network properties I
6. Network properties II
7. Community detection I
8. Community detection II
9. Community detection III
10. Epidemics and information propagation I
11. Epidemics and information propagation II
12. Dynamic networks I
13. Dynamic networks II
14. Link prediction
Course Learning Outcomes 1. Notion of complex system
2. Topological properties
3. Methods for the analysis of networks
4. Use of the R language and iGraph library
Teaching and Learning Methods project=50%, final=50%
References • M. E. J. Newman, The structure and function of complex networks, SIAM Review 45:167-256,2003.
• R. Albert and A.-L. Barabasi Statistical mechanics of complex networks. Rev. Mod. Phys., 74(1), 2002.
• S. N. Dorogovtsev, Lectures on Complex Networks, Oxford University Press, 2010.
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 Introduction
2 Basic graph theoretical notions
3 Random graphs and models I
4 Random graphs and models II
5 Network properties I
6 Network properties II
7 Community detection I
8 Community detection II
9 Community detection III
10 Epidemics and information propagation I
11 Epidemics and information propagation II
12 Dynamic networks I
13 Dynamic networks II
14 Link prediction
Practice Topics
Week Weekly Contents
Assessment System
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 1 50
Contribution of final exam to overall grade 1 50
Toplam 2 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 0 0
Project 1 100
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 1 100
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X
9 X
10 X
11 X
12 X
13 X
ECTS
Activities Number Period Total Workload
Class Hours 14 3 42
Working Hours out of Class 12 2 24
Project 12 10 120
Final Examinations (including preparation) 1 10 10
Total Workload 196
Total Workload / 25 7,84
Credits ECTS 8
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