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ATUSS

VISER

Academy of Technical and Art Applied Studies

School of Electrical and Computer Engineering

Social networks analysis Course code: OI0021 | 6 ECTS credits

Basic information
Level of Studies: Undergraduate applied studies
Year of Study: 2
Semester: 3
Requirements: -
Goal: Understanding general concepts and tecnological infrastructure of social networks and social computing; acquiring theoretical and practical knowledge related to the social networking; introduction to the data analysis and information searching in social networks.
Outcome: After successfuly completed course, the student should be able to understands general concepts and tecnological infrastructure of social networks and social computing. Student should be able to search and analyse data in social networks, use and take part in develop of modern tecnologies related to social networks and partitive web.
Contents of the course
Theoretical instruction:
  1. Introductory lecture. Basic terms.
  2. Development of WWW. WEB 2.0 and WEB 3.0.
  3. Types and caracteristics of social networks.
  4. Open source initiative. Open data, open content. .
  5. Social network data analysis.
  6. Big data analysis.
  7. Summary lesson.
  8. Social software.
  9. Social network analysis (graph theory and social network).
  10. Social processing of information. Information searching and navigation.
  11. Recommendation system.
  12. Trust and reputation increasing mechanisms.
  13. Social computing future.
  14. Summary lesson
Practical instruction (Problem solving sessions/Lab work/Practical training):
  1. Practical instructions in computer laboratories: follow theoretical lessons. Practical work with platforms for creating private social networks. Practical work with social network analysis tools. Course sillabus follows recomendations of IEEE/ACM Computing Curriculum: IT2008 IT body of knowledge: WS. Social software.
Textbooks and References
  1. Tara Calishain, Rael Dornfest, Google trikovi, Kompjuter biblioteka, 2006.
  2. Hiroshi Ishikawa, Social Big Data Mining, CRC Press, 2015.
  3. Matthew A. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O'Reilly, 2011.
  4. D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, New York, NY, USA, 2010.
  5. M. Tsvetovat and A. Kouznetsov, Social Network Analysis for Startups: Finding connections on the social web, O’Reilly Media, 2011.
Number of active classes (weekly)
Lectures: 2
Practical classes: 3
Other types of classes: 0
Grading (maximum number of points: 100)
Pre-exam obligations
Points
activities during lectures
10
activities on practial excersises
10
seminary work
25
colloquium
0
Final exam
Points
Written exam
55
Oral exam
0

Lecturer

Associate