Social Media Analytics Course
Main Lecturer: Mourad Khayati
Co-instructors: Philippe Cudré-Mauroux and Julien Audiffren
Teaching Assistant: Manuel Mondal
Level: Master (5 ECTS), Spring 2026
Location: Fribourg
Overview
How does information spread? How can we predict social interactions? Addressing these questions requires analyzing massive volumes of data. Many complex datasets can be naturally represented as graphs that capture relationships and interactions among entities. Graph-structured data can then be processed by advanced algorithms to perform tasks including classification, clustering, and forecasting. This course examines the computational, algorithmic, and modeling challenges involved in analyzing large-scale social graphs. By exploring the structural properties of those graphs, you will learn how to apply machine learning and data mining techniques to uncover patterns, improve predictions, and gain insights across a wide range of networked systems.
The course begins with an overview of the fundamental concepts in social media and network analytics, including graph-based data representations, similarity and centrality measures, network metrics, and graph storage and processing tools. Building on this foundation, the course explores a range of algorithms for analyzing network structure, with particular emphasis on community detection, as well as methods for predicting missing or future links and nodes in social networks. Students will then study additional important applications of social network analysis, such as information diffusion and influence modeling, social recommendation systems, social and network mining, and knowledge graphs. Throughout the course, both theoretical principles and practical applications will be emphasized to provide students with a solid understanding of how social network analytics is applied in real-world scenarios.
The course is structured to include both a deep theoretical component and a hands-on practical component. The theoretical part focuses on the fundamental principles, models, and algorithms underlying social network analysis and social media analytics. Complementing this, the practical part is delivered through a semester-long project in which students apply the concepts and techniques learned in class to real-world social network data. Through this project, students gain experience in data collection, analysis, implementation, and evaluation, bridging theory and practice and developing practical problem-solving skills.
Learning Outcomes
Upon successful completion of this course, you will be able to:
- Collect, manage, and store social network data, and analyze network structures to identify their key properties and metrics.
- Apply clustering and community detection techniques to discover and interpret social communities within large-scale networks.
- Apply graph completion techniques to infer missing nodes, links, and attributes, and to reconstruct incomplete or partially observed social networks.
- Design and evaluate models to analyze, predict, and optimize information diffusion and influence processes in social networks.
- Enhance and improve recommendation systems by effectively incorporating social network information and relationships.
Organization
Teaching format: This course consists of lectures and hands-on exercises/labs. The weekly/bi-weekly exercises are an important part of the learning process. Solving the exercises will be the best way to prepare for the final exam.
The lectures take place Tuesday 14:15–17:00 in room E130 (UniFR, PER21). The lecture notes for the course will become available as we progress through the semester.
The number of participants in the course will be limited to ensure adequate supervision for all projects. Only students who pass the placement test will be eligible to continue. The list of admitted participants will be communicated on March 6, 2026.
References: The course will use the following textbook
- [B1] Social Data Mining: An Introduction, Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu, First edition, Cambridge University Press, 2014. (link)
Additionally, we will use the following books in some lectures
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[B2] Graph Representation Learning, William L. Hamilton, Morgan & Claypool Publishers, 2020 (link)
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[B3] Network Science, Albert-László Barabási, Cambridge university press, 2016 (link)
Project
As part of the course assessment, students must complete a semester-long project. The general aim is to analyze a large social media dataset and uncover its underlying characteristics. Students must develop a tool that integrates the entire social data pipeline, from data collection through analytical visualization. This pipeline should include all major stages of social media data processing, including data collection (e.g., via APIs, scraping, or provided datasets), data cleaning and preprocessing, data storage, analytical processing, and interactive visualization of the results. The final system should function as an integrated tool rather than a collection of disconnected components.
A core requirement of the project is the implementation of social graph analytics from scratch. Students are expected to develop key graph-based methods without relying on high-level library implementations. These custom implementations must then be evaluated and benchmarked against existing solutions, which may include algorithms discussed in the course, methods from the research literature, or widely used external libraries. The evaluation should consider factors such as accuracy, scalability, computational efficiency, and practical usability.
Students must document their design choices, methodological assumptions, and experimental results in a clear and well-structured report. The project will be assessed based on technical correctness, depth of analysis, originality, clarity of presentation, and the quality of the evaluation. The semester-long project constitutes 30% of the final course grade.
The full description of the project can be found on this page.
Syllabus
- Lect 01: Social Media Analytics Basics
- Lect 02: Graph Management (Chap 2 in B1)
- Lect 03: Network Measures (Chap 3 in B1)
- Lect 04: Community Detection (Chap 6 in B1 and Chap 9 in B3)
- Lect 05: Graph Completion (Part I in B2)
- Lect 06: Diffusion & Assortativity (Chap 7 and 8 in B1)
- Lect 07: Social Recommendation (Chap 9 in B1)
- Lect 08: Social Graph Mining (Chap 5 in B1)
- Lect 09: Knowledge Graphs (open lecture)
- Lect 10: Network Models (Chap 4 in B1)
- Lect 11: Recap
Tentative Schedule
| Date | Lecture | Exercises |
|---|---|---|
| 17/02/2026 | sl01 – Intro and SMA Basics (M. Khayati) | Intro Projects and Examples |
| 24/02/2026 | sl02 – Graph Management (M. Khayati) | ex01 – Graph Processing Tools |
| 03/03/2026 | sl03 – Network Measures (M. Khayati) | ex02 – Graph Management |
| 10/03/2026 | Project – Idea Presentations (M. Khayati) | |
| 17/03/2026 | sl04 – Community Detection / 1 (M. Khayati) | ex03 – Network Measures |
| 24/03/2026 | sl04 – Community Detection / 2 (M. Khayati) | Check point 1 (90min) |
| 31/03/2026 | sl05 – Graph Completion (M. Mondal) | ex04 – Community Detection |
| 07/04/2026 | Easter Holidays | |
| 14/04/2026 | sl06 – Diffusion / Influence (P. C-Mauroux) | ex05 – Graph Completion |
| 21/04/2026 | sl07 – Social Recommendation (J. Audiffren) | ex06 – Diffusion / Influence |
| 28/04/2026 | sl08 – Social Graph Mining (M. Khayati) | ex07 – Social Recommendation |
| 05/05/2026 | sl09 – Knowledge Graphs (Guest) | Check point 2 (90min) |
| 12/05/2026 | sl10 – Network Models (P. C-Mauroux) | ex08 – Social Graph Mining |
| 19/05/2026 | sl11 – Recap (M. Khayati) | ex09 – Network Models |
| 26/05/2026 | Project – Final Presentations (M. Khayati) |
The schedule might slightly change depending on the availability of the lecturers.
Course material will be published on ILIAS.