COURSE UNIT TITLE

: SOCIAL NETWORK ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5029 SOCIAL NETWORK ANALYSIS 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

ASSOCIATE PROFESSOR ZEYNEP NIHAN BERBERLER

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

To analyze networks based on various centrality measures defined in literature with applications to computer science. To give a knowledge of basic concepts of networks. To solvecomputer science problems and problems of different type of disciplines by using network centrality concepts. To design efficient polynomial time algorithms to find the centrality measures of networks by using graph theory concepts.

Learning Outcomes of the Course Unit

1   Have a knowledge of basic concepts of social network analysis.
2   Be able to analyze social networks based on centrality measures.
3   Be able to solve computer science problems by using centrality measure concepts.
4   Be able to design efficient polynomial time algorithms to measure various centralities of networks by using graph theory concepts.
5   Be able to analyze and make comparisons between centrality measures.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, notation and definitions
2 Network efficiency
3 Network centrality concepts
4 Degree centrality, betweenness centrality
5 Closeness centrality
6 Eigenvector centrality
7 PageRank centrality
8 Centralization
9 Midterm exam
10 Algorithmic aspects of centrality measures
11 Algorithmic aspects of centrality measures
12 Comparisons between centrality measures
13 Node residual closeness
14 Link residual closeness

Recomended or Required Reading

L.C. Freeman, Centrality in social networks: conceptual clarification, Social Networks, (1979).
West D.B., Introduction to Graph Theory, Prentice Hall, NJ (2001).
V. Latora, M. Marchiori, Efficient behavior of small-world networks, Phys. Rev. Lett., (2001).

Planned Learning Activities and Teaching Methods

The course is given by the lecturer generally, some periods of the course will continue interactively.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST * 0.60


*** 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)

zeynep.berberler@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 50 50
Preparation for final exam 1 60 60
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 205

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1444
LO.2444
LO.3444
LO.4444
LO.5444