Course: 2024/2025

Network analysis and data visualization

(17242)

Requirements (Subjects that are assumed to be known)

It is recommended to have completed the Mathematics, Statistics subjects and a good level in programming in R or Python

Basic Skills
- Acquisition of knowledge and skills that provide with a background of creativity in the development and application of ideas, often within a research context.
- Ability to apply acquired knowledge and to solve problems under novel or almost novel situations or within broader (multidisciplinary) contexts related with big data.
- Acquisition of skills for learning in an autonomous and continuous manner.
General Skills
- Ability to apply the theoretical foundation of collect, storage, processing and presentation of information, especially for big data volumes.
- Ability to identify the most suitable data analysis technique in each problem, and to apply it for obtaining the most appropriate solution to each one.
- Ability to obtain practical and efficient solution for processing of big data volumes.
- Skill to synthesize data analysis conclusions, and to communicate it clearly and convincingly in a bilingual environment.
- Ability to generate new ideas and to anticipate new situations, within the context of data analysis and decision making.
- Skill to working collaboratively and to collaborate with others autonomously.
Specific Skills
- Skill to design data processing systems, from the data gathering to statistical analysis and presentation of final results.
- Ability to apply the basic principles of network science and apply them to the study of different data to model and forecast their behavior using features extracted from network science.
- Ability to design effective visualizations of large data sets that can lead to the discover, interpretation and access to those datasets..
- Ability to identify the opportunity to apply network science and visualization techniques for solving real problems.
Learning outcomes
- Basic knowledge about network science techniques.
- Understanding of basic network science techniques.
- Making practical use of network science techniques in real problems
- Basic knowledge of data visualization techniques
- Ability to use visualization techniques to explain and solve real problems

Skills and learning outcomes

Description of contents: programme

1. Networks: general concepts and definitions
1.1 Network introduction
- Network importance and examples
- Historical background of network science
- Network types and attributes
1.2 Network measures
- Degree distributions and correlations
- Transitivity and clustering coefficient
- Connectedness and giant component
Workshop 1: Gephi (network visualization)
2. Network communities
2.1 Centrality measures
- Distances on networks, radius and diameter
- Degree, closeness, harmonic and betweenness centralities
- Eigenvector, Katz and PageRank centralities
2.2 Network mesoscale analysis
- Cliques and network motifs
- Modularity measure
- Community detection algorithms
Workshop 2: iGraph and graph visualization (R)
3. Network models
3.1 Random network models
- Erdoös-Rényi (ER) random graph
- Random Geometric Graph (RGG)
- Configuration network models
3.2 Simple rule network models
- Stochastic block model
- Barabási-Albert (BA) scale-free network model
- Watts-Strogatz (WS) small-world network model
Workshop 3: Netlogo, community detection algorithms and network models (R)
4. Social Networks
4.1 Local and global properties of social networks
- Examples of social networks and their properties
- (Generalized) Friendship paradox
- Six degrees of separation
- Dunbar¿s numbers
4.2 Social mechanisms
- Homophily
- Triadic closure
- Strength of relationships
Workshop 4: Network analysis
5. Network dynamics and applications
5.1 Link prediction
- Assortative, relational and proximity algorithms
- Graph distance methods
- Common neighbors methods
- Preferential attachment
- Katz score and hitting time
- Community-based heuristics
5.2 Spreading processes
- Susceptible-Infected (SI) model
- Susceptible-Infected-Removed (SIR) model
- Susceptible-Exposed-Infected-Removed (SEIR) model
- More advanced models
Workshop 5: Link prediction and spreading processes
6.1 Data visualization
6.1 Introduction to visualization
- Types of visualizations
- Examples of good visualizations
- Examples of bad visualizations
6.2 Introduction to data and charts
- Types of data
- Types of charts
- Visualization tools
Workshop 6: Data visualization (ggplot)
Workshop 7: GoogleVis, R shiny app and geolocalised data visualization

Learning activities and methodology

The course is imparted in specific rooms and laboratories for the Master Program. It will include:
- Lectures for the presentation, development and analysis of the contents of the course.
- Practical sessions for the resolution of individual problems and practical projects in the laboratory
- Seminars for discussion with reduced groups of students

Assessment System

- % end-of-term-examination 40
- % of continuous assessment (assigments, laboratory, practicals...) 60

Calendar of Continuous assessment

Basic Bibliography

- A-L Barabasi. Network science. http://barabasi.com/book/networkscience#networkscience. 2018
- E. Tufte. The Visual Display of Quantitative Information (2nd Edition).. Graphic Press. 2001
- M.E.J. Newman. Networks: An Introduction . Oxford University Press. 2010
- Rafa Donahue. Fundamental Statistical Concepts in Presenting Data. http://biostat.mc.vanderbilt.edu/wiki/Main/RafeDonahue. 2018

Additional Bibliography

- Alberto Cairo. The Truthful Art: Data, Charts, and Maps for Communication. New Riders. 2016
- Nathan Yau. Visualize This. John Wiley & Sons. 2011

The course syllabus may change due academic events or other reasons.