Social Media Analytics

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.

Have also a look at this website: https://mkhayati.github.io/courses/sma/

Details

Code	63091
Type	Course
ECTS	5
Site	Fribourg
Track(s)	T6 – Data Science
Semester	S2026

Teaching

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.
Lecturer(s)	Mourad Khayati Philippe Cudré-Mauroux
Language	english
Course Page	The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_3419480.html.

Schedules and Rooms

Period	Weekly
Schedule	Tuesday, 14:15 - 17:00
Location	UniFR, PER21
Room	E130

Additional information

Comment

First Lecture The first lecture will be announced later.