Checking date: 17/04/2024

Course: 2024/2025

Network Analysis
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)

Coordinating teacher: GALEANO SAN MIGUEL, PEDRO

Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Mathematics for Data Science Probability Statistical Inference Programming in R Numerical Methods for Data Science Multivariate Statistics
The basic objectives of the subject are: 1. Understand the basics of networks, graphs, and families of graphs. Learn the concept of adjacency matrices and their significance. 2. Network Visualization: Gain proficiency in designing and visualizing networks. Explore techniques for decorating and handling large networks. 3. Descriptive Analysis of Networks: Analyze network structures through vertex and edge characteristics. Identify influential vertices, assess cohesion, detect communities, and understand assortativity. Explore real-world applications of network analysis. 4. Models and Inference for Networks: Familiarize with classical and generalized models for network representation. Study small world models and their applications. 5. Prediction in Networks: Learn methods for predicting network interactions using nearest neighbor approaches. Explore alternative prediction methods in network contexts.
Skills and learning outcomes
Description of contents: programme
1. Introduction and preliminaries. 1.1 Introduction. 1.2 Examples of networks. 1.3 Graphs. 1.4 Families of graphs. 1.5 The adjacency matrix. 2. Network visualization. 2.1 Introduction. 2.2 Network design. 2.3 Decorating networks. 2.4 Large networks. 3. Descriptive analysis of networks. 3.1 Introduction. 3.2 Characteristics of vertices: centrality, influencers, ... 3.3 Characteristics of the edges: centrality. 3.4 Cohesion of networks. 3.5 Detection of communities in networks. 3.6 Assortativity. 3.7 Applications. 4. Models and inference for networks. 4.1 Introduction. 4.2 Classical models. 4.3 Generalized models. 4.4 Small world models. 4.5 Applications. 5. Prediction in networks. 5.1 Introduction. 5.2 Methods of nearest neighbors. 5.3 Alternatives.
Learning activities and methodology
Learning activities: Theoretical classes Practical classes Tutorials Team work Individual work of the student In-person evaluation tests Methodology to be used: Theoretical classes with support material available on the Web. Problem solving classes. Computational practices in computer rooms. Oral exhibitions Tutorial regime: Individual tutorials throughout the course.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Albert László Barabási. Network Science. Cambridge University Press. 2016
  • Erci D. Kolaczyk. Statistical Analysis of Network Data. Springer. 2009

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