Social Network Analysis

Overview

This course introduces students to the paradigm of network science and its application across multiple domains. Students learn to think relationally — understanding phenomena not as isolated entities but as interconnected systems of relationships. The course covers the theoretical foundations of network analysis alongside practical skills for analyzing and visualizing networks using R.

What You Will Learn

The course spans the fundamentals of network science: graph theory basics, network metrics (centrality, density, reciprocity, clustering), community detection algorithms, network visualization techniques, and advanced topics such as temporal networks and diffusion processes. Students work with diverse types of networks — social, collaboration, knowledge, and information networks — drawn from education, social media, and other domains.

Format

The course combines lectures with intensive practical lab sessions where students analyze real network datasets. The centerpiece is a course project where students collect their own network data, analyze it using the methods learned in class, and present their findings. Assessment is based on practical assignments, the network analysis project, and peer presentations. No final exam.

Who Should Take This Course

The course is open to University of Eastern Finland students and visiting students. It is suitable for anyone interested in understanding relational structures in data — whether in education, social sciences, health, or technology. Basic familiarity with R is helpful but not strictly required.