COURSE UNIT TITLE

: COMPUTATIONAL NETWORK DESIGN

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5030 COMPUTATIONAL NETWORK DESIGN ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

This course aims to provide a wide perspective in network science topics for analyzing the dynamics and topology of complex networks by use of algorithmic, computational, and statistical methods.

Learning Outcomes of the Course Unit

1   Develop and implement representative mathematical models of a network
2   Design and measure relevant network characteristics
3   Analyze and optimize computational performance of complex networks
4   Plan, design and implement algorithmic approaches for network analysis
5   Be able to develop applied research projects in network science

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Representing networks, Structures and notation
2 Network flow models Homework 1
3 Measuring networks, Network statistics
4 Combinatorial analysis methods for networks
5 Computational methods for spatial analysis Homework 2
6 Social networks
7 Structural balance
8 Midterm exam
9 Game theoretic models, Evolutionary game theory
10 Network traffic modeling
11 Interaction in networks Homework 3
12 Information networks and web
13 Network dynamics, Population models
14 Network dynamics, Structural models
15 Project presentations

Recomended or Required Reading

Textbook(s): M.S. Bazaraa, J.J. Jarvis, and H.D. Sherali, Linear Programming and Network Flows, Wiley-Interscience, 2004.
D. Easley, and J. Kleinberg, Networks, Crowds, and Markets, Cambridge Univ. Press, 2010.
Supplementary Book(s): A. Okabe, and K. Sugihara, Spatial Analysis along Networks: Statistical and Computational Methods, Wiley, 2012.
M. Jackson, Social and Economic Networks, Princeton University Press, 2008.
J. Golbeck, Analyzing the Social Web, Morgan Kaufmann Publications, 2013.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 RPR RESEARCH PRESENTATION
4 PAR PARTICIPATION
5 FCG FINAL COURSE GRADE MTE* 0.20 + ASG * 0.20 + RPR * 0.50 + PAR * 0.10


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

ugur.eliiyi@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 3 39
Project Preparation 1 40 40
Preparing presentations 1 10 10
Preparing assignments 3 15 45
Preparation for midterm exam 1 10 10
Preparing report 1 20 20
Midterm 1 3 3
TOTAL WORKLOAD (hours) 209

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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