Objectives and outcomes
Students acquire knowledge and practical skills in algorithms for social network analysis. Students
understand modern algorithms for social network analysis, including their structure and content.
They can apply social network analysis algorithms to relevant domains such as recommendation system
implementation.
Lectures
Networks and graph theory. Examples of social networks. Online social networks. The social network of
scientific-research collaboration. Social network visualisation. Application of graph theory in the analysis
of social networks: distance, search, group detection, impact analysis, metrics. Application of statistical
methods in the analysis of social networks. Text processing in the domain of social networks. Sentiment
classification. Information extraction. Social network clustering. Predictive behavioue. Recommendation
systems based on social media analysis.
Practical classes
An overview of software tools for analysing and visualising large networks. Selection of domains for
social network analysis. Data retrieval and extraction of relevant elements for social network analysis.
Analysis of the social network structure. Social network content analysis. Social network visualisation.
Statistical calculations of the social network. Application of graph theory algorithms to social networks.
Application of different metrics in social network analysis. Social network clustering. Implementation of a
recommendation system based on social network analysis.