Checking date: 18/05/2022

Course: 2022/2023

Social Network Analysis
Study: M. Computational Social Science (375)

Coordinating teacher: SANCHEZ SANCHEZ, ANGEL

Department assigned to the subject: Mathematics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Data Programming (19138) Data Visualization (19139) Data Harvesting (19145) Statistics and Data Science I (19140)
Core Competences: - Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context - Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. - Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. - Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. - Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General Competences: - Ability to understand and analyze the main global social theories and how they are changing with the application of computational tools. - Ability to identify, define and formulate social science problems and solve them using computational techniques. This includes the ability to assess all the factors involved, not only technical but also legal. - Ability to compile and analyze existing knowledge in the different areas of computational social sciences, and to propose possible solutions to the problems raised. - Ability to apply theoretical and methodological knowledge of computational social sciences to the analysis and resolution of specific cases and empirical problems. - Ability to address issues raised under new or unfamiliar environments, within the context of computational social sciences. - Ability to plan and carry out research in the field of computational social sciences in an autonomous way. - Ability to communicate and present, in a clear, precise and rigorous manner, concepts and results related to computational social science activities to both specialized and non-specialized audiences. Specific Competences: - Ability to understand and analyze the main theoretical-methodological approaches of computational social sciences, their potentials and limitations, and to apply them to the analysis of specific social problems. - Ability to develop an experimental/causal research design appropriate to the research questions. - Ability to lead and supervise interdisciplinary teams in the field of computational social sciences. Learning Outcomes: - Ability to explain basic concepts and theories of social networks and understand how these concepts and theories can explain behaviors of different actors, as well as aggregate outcomes of those behaviors. - Ability to critically examine how networks can contribute to the explanation of social, political, economic and cultural phenomena. - Ability to use statistical software to visualize networks and analyze their properties, connecting them to these concepts and theories. - Ability to explain the principles underlying statistical models for social networks.
Skills and learning outcomes
Description of contents: programme
1. Introduction, definitions and main concepts 1.1. "Linked" data. Social processes that can be represented as networks 1.2. Definition of network and random networks 2. Measures for relational analysis of social network data 2.1. Statistical analysis of social networks 2.2. Local measures: degree, transitivity, asortativity 2.3. Centrality in social networks 2.4. Structure of communities and small world phenomena 2.5. Importance of strong and weak links 3. Structure and behavior of networks 3.1. Social contagion processes. Homophilia vs. influence 3.2. Formation of links, network dynamics 3.3. Polarization and coordination in social networks 3.4. Resilience of social networks 4. Visualization of social networks 4.1. Introduction to social media display packages 4.2. Workshop on visualization of social networks on Twitter 5. Practical examples 5.1. Analysis of conversations in social networks 5.2. Models of epidemic propagation in networks 5.3. Marketing in social networks 5.4. Segregation in social networks and urban areas
Learning activities and methodology
Training Activities: - Theoretical-practical classes - Laboratory practical sessions - Tutorials - Group work - Partial and final examinations Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group. - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. - Developing pieces of work and reports, individually or in group. - Seminars/lectures by national and international experts, in face-to-face or remote synchronous sessions.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Basic Bibliography
  • A.L. Barabási. Network Science. Cambridge University Press. 2016
  • Networks, Crowds and Markets. D. Easley and J. Kleinberg. Cambridge Univ Press. 2010
  • Networks: An Introduction (1st Edition). M.E.J. Newman. Oxford University Press. 2010
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • A. Barrat, M. Barthelemy and A. Vespignani. Dynamical Processes on Complex Networks. Cambridge University Press. 2008
  • Bit by Bit: Social Research in the Digital Age. Matthew J. Salganik . Princeton University Press. 2017
  • M.O. Jackson. Social and economic networks. Princeton University Press. 2010
  • The structure and function of complex networks SIAM Review 45, 167-256 (2003). M.E.J. Newman. SIAM. 2003
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

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