CS519 Social Network Analysis
CS519 Social Network Analysis
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Computer Sciences and Engineering
Course Objectives
On successful completion of this course the student will be able to critically analyze and evaluate social networks and and present social network analysis concepts in a concise manner to an audience.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Analyzing Social Networks, Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Sage, Supplimentary book: Social Network Analysis: Methods and Applications, Stanley Wasserman and Katherine Faust, Cambridge University Press.
Additional Literature
Teaching Methods
The student is expected to carry out a semester long project in order to demonstrate the skills required to implement a real-world project
First 9 weeks, there will be 3 hours of lectures including project guidence
Afterwards, there will be regular consulation meetings with the students in order to guide and evaluate the term project
Students are also expected to perform a research on High Dynamic Range imaging topic and to write and present a research paper at the end of the semester
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction and brief overview, Basic concepts | Chapters 1, 2 |
| 2 | Concepts of data management | Chapter 5 |
| 3 | Graph-based network visualization | Chapter 7 |
| 4 | Cohesion, centralization, core-periphery | Chapter 9 |
| 5 | Node level measures | Chapter 10 |
| 6 | Statistical measures, multivariate, Discuss graph measrues versus statistical measures, trade-offs | Chapter 6 |
| 7 | Creating subgroups | Chapter 11 |
| 8 | Ego networks | Chapter 15 |
| 9 | Triad analysis, roles and equivalence | Chapter 12 |
| 10 | Testing hypotheses, longitudinal analysis | Chapter 8 |
| 11 | Network measures as independent and dependent variables | Chapter 13 |
| 12 | Social network theoretical concepts - social capital, diffusion, small world, reciprocity, social support | Handouts |
| 13 | Paper presentations. | |
| 14 | Project presentations and Final review | |
| 15 |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Project
AI: Not AllowedAlignment with Learning Outcomes :
Research paper
AI: Not AllowedAlignment with Learning Outcomes :
Participation
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
42 hours ⏳ (14 week × 3 h)
Project
48 hours ⏳ (12 week × 4 h)
Research
36 hours ⏳ (12 week × 3 h)
Home study
14 hours ⏳ (14 week × 1 h)
Final exam study
8 hours ⏳ (1 week × 8 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [CS519] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 35 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Project | 1 | 40 | Not Allowed | ||
| Research paper | 1 | 20 | Not Allowed | ||
| Participation | 1 | 5 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Project | 4 | 12 | 48 | |||
| Research | 3 | 12 | 36 | Home study | 1 | 14 | 14 | |||
| Final exam study | 8 | 1 | 8 | |||||||
| Total Workload Hours = | 148 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||