Guías Docentes Electrónicas
1. General information
Course:
FÍSICA DE REDES COMPLEJAS Y APLICACIONES INTERDISCIPLINARES
Code:
310936
Type:
ELECTIVE
ECTS credits:
6
Degree:
2351 -
Academic year:
2019-20
Center:
602 -
Group(s):
20 
Year:
1
Duration:
C2
Main language:
Spanish
Second language:
English
Use of additional languages:
English Friendly:
Y
Web site:
Bilingual:
N
Lecturer: JUAN ANGEL ALEDO SANCHEZ - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Infante Don Juan Manuel, office 0.C.1
MATEMÁTICAS
2189
juanangel.aledo@uclm.es
Se anunciará en la plataforma virtual

Lecturer: GUILLERMO MANJABACAS TENDERO - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Infante Don Juan Manuel, office 1.B.4
MATEMÁTICAS
2472
guillermo.manjabacas@uclm.es
Will be available on www.esiiab.uclm.es/tutorias.php

Lecturer: SILVIA MARTINEZ SANAHUJA - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Facultad de Educación
MATEMÁTICAS
2546
silvia.msanahuja@uclm.es
To be determined. See the website of the Faculty.

2. Pre-Requisites

This course is self-contained so there is no need to have studied previously any contents related to this subject.

As a general enrollment requirement, the provisions of RD 1393/2007, as amended by RD 861/2010, were taken into account in order to define the entry requirements for the Master. Candidates must certify that they hold a bachelor's degree or equivalent in order to be eligible for admission to the master. International students from outside the EHEA must certify that they are qualified for admission to postgraduate courses.

Students admitted to this degree program are required to hold a bachelor's degree or equivalent in Mathematics or Physics. Other graduates with knowledge and skills of a level equivalent to holders of bachelor of engineering or science degrees are also eligible for admission to this master's degree.

3. Justification in the curriculum, relation to other subjects and to the profession

Nowadays, we can observe a clear tendency for interdisciplinary studies.The mathematical notions of complex networks have fruitful applications to other fields of knowledge. The understanding of reality through models based on networks is used, for example, in Engineering, Computation, Medicine, Biology, Ecology or Social Sciences. One of the aims of this course is to provide the basics that could be developped later depending on the interests of the students.

The topics of this course include: neural, biology, social and economic networks, useful in many branches of knowledge.

The course is also guided by the idea that students at this level must be able to become autonomous learners taking into account their future prospects to undertake research.


4. Degree competences achieved in this course
Course competences
Code Description
CB06 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB07 Apply the achieved knowledge and ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to the area of study
CB08 Be able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of knowledge and judgments
CB09 Know how to communicate the conclusions and their supported knowledge and ultimate reasons to specialized and non-specialized audiences in a clear and unambiguous way
CB10 Have the learning skills which allow to continue studying in a self-directed or autonomous way
CE01 Solve physical and mathematical problems, planning their solutions based on the available tools and time and resource constraints
CE02 Develop the ability to decide the appropriate techniques to solve a specific problem with special emphasis on those problems associated with the Modeling in Science and Engineering, Astrophysics, Physics, and Mathematics
CE03 Have the ability to build and develop advanced mathematical reasoning, and delve into the different fields of mathematics
CE04 Have the ability to build and develop advanced physical reasoning, and delve into the various fields of physics and astrophysics
CE05 Know how to obtain and interpret physical and/or mathematical data that can be applied in other branches of knowledge
CE06 Prove the necessary capacity to perform a critical analysis, evaluation and synthesis of new and complex results and ideas in the field of astrophysics, physics, mathematics and biomathematics
CE07 Ability to understand and apply advanced knowledge of mathematics and numerical or computational methods to problems of biology, physics and astrophysics, as well as to build and develop mathematical models in science, biology and engineering
CE08 Ability to model, interpret and predict from experimental observations and numerical data
CG01 Know how to work in a multidisciplinary team and manage work time
CG02 Ability to generate and independently develop innovative and competitive proposals in research and professional activity in the scientific field of Physics and Mathematics
CG03 Present publicly the research results or technical reports, to communicate the conclusions to a specialized court, interested persons or organizations, and discuss with their members any aspect related to them
CG04 Know how to communicate with the academic and scientific community as a whole, with the company and with society in general about Physics and/or Mathematics and its academic, productive or social implications
CG05 Gain the ability to develop a scientific research work independently and in its entirety. Be able to search and assimilate scientific literature, formulate hypotheses, raise and develop problems and draw conclusions from the obtained results
CG06 Gain the capacity for dialogue and cooperation with scientific and business communities from other fields of research, including social and natural sciences
CT01 Promote the innovative, creative and enterprising spirit
CT02 Guarantee and promote respect for Human Rights and the principles of equality, universal accessibility, non-discrimination and democratic values and the culture of peace
CT03 Develop critical reasoning and the ability to criticize and self-criticize
CT04 Understand and reinforce the ethical and deontological responsibility and commitment in the performance of the professional and research activity and as a citizen
CT05 Autonomous learning and responsibility (analysis, synthesis, initiative and teamwork)
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Understanding of the concept of probability distribution of nodes and of correlations between nodes in complex networks
Understanding of the complex network concept in physics and mathematics, in particular the concept of random graph, scale invariant network, small world network and multiplex networks
Ability to simulate different types of complex networks by computer and to study their emerging properties
Understand the dynamics of complex networks generated by preferential attachment
Understand the underlying physics and emerging phenomena in complex neural networks
Understand the underlying physics and emerging phenomena in other complex networks such as trophic networks and metabolic networks
Understand the underlying physics and emerging phenomena in complex social networks. Understand the dynamics of the structure of social networks
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Introduction to complex networks
  • Unit 2: Neural networks
  • Unit 3: Networks in systems biology. Boolean networks
  • Unit 4: Networks in ecology
  • Unit 5: Social and economic networks
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences ECTS Hours As Com R Description
Class Attendance (theory) [ON-SITE] Combination of methods CB06 CB07 CB09 CB10 CE01 CE02 CE05 CE07 CE08 CT03 CT05 1.48 37 Y N N
Class Attendance (practical) [ON-SITE] Combination of methods CB06 CB07 CB09 CB10 CE01 CE02 CE05 CE07 CE08 CT03 CT05 0.16 4 Y N N
Workshops or seminars [ON-SITE] Workshops and Seminars CE06 CG06 0.04 1 Y N N
Writing of reports or projects [OFF-SITE] Individual presentation of projects and reports CB07 CB08 CB09 CB10 CG01 CG02 CG03 CG04 CG05 CT01 CT02 CT03 CT04 CT05 2.16 54 Y Y Y
Study and Exam Preparation [OFF-SITE] Self-study CB06 CB07 CB08 CB09 CB10 CE01 CE02 CE04 CE05 CE06 CE07 CG01 CG02 CG05 CT01 CT03 CT04 CT05 2.16 54 Y N Y
Total: 6 150
Total credits of in-class work: 1.68 Total class time hours: 42
Total credits of out of class work: 4.32 Total hours of out of class work: 108
As: Assessable training activity
Com: Training activity of compulsory overcoming
R: Rescheduling training activity
8. Evaluation criteria and Grading System
  Grading System  
Evaluation System Face-to-Face Self-Study Student Description
Assessment of problem solving and/or case studies 80.00% 80.00% Students must solve individually some problems related with the contents of the course.
Theoretical papers assessment 10.00% 10.00% Students must write individually a report about some topics of the course.
Other methods of assessment 10.00% 10.00% Attendance and participation in the possible seminars of the course will be taken into account.
Total: 100.00% 100.00%  

Evaluation criteria for the final exam:
The final grade depends on the partial marks obtained from the exercises and other assignments (the deadlines will be published on Campus Virtual).
Specifications for the resit/retake exam:
Same conditions as before.
Specifications for the second resit / retake exam:
Same conditions as before.
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours
Workshops or seminars [PRESENCIAL][Workshops and Seminars] 1

Unit 1 (de 5): Introduction to complex networks
Activities Hours
Class Attendance (theory) [PRESENCIAL][Combination of methods] 13
Class Attendance (practical) [PRESENCIAL][Combination of methods] 1
Writing of reports or projects [AUTÓNOMA][Individual presentation of projects and reports] 17
Study and Exam Preparation [AUTÓNOMA][Self-study] 17
Teaching period: Weeks 1-5

Unit 2 (de 5): Neural networks
Activities Hours
Class Attendance (theory) [PRESENCIAL][Combination of methods] 4
Writing of reports or projects [AUTÓNOMA][Individual presentation of projects and reports] 9
Study and Exam Preparation [AUTÓNOMA][Self-study] 9
Teaching period: Weeks 6-7

Unit 3 (de 5): Networks in systems biology. Boolean networks
Activities Hours
Class Attendance (theory) [PRESENCIAL][Combination of methods] 5
Class Attendance (practical) [PRESENCIAL][Combination of methods] 1
Writing of reports or projects [AUTÓNOMA][Individual presentation of projects and reports] 7
Study and Exam Preparation [AUTÓNOMA][Self-study] 7
Teaching period: Weeks 8-9

Unit 4 (de 5): Networks in ecology
Activities Hours
Class Attendance (theory) [PRESENCIAL][Combination of methods] 5
Class Attendance (practical) [PRESENCIAL][Combination of methods] 1
Writing of reports or projects [AUTÓNOMA][Individual presentation of projects and reports] 7
Study and Exam Preparation [AUTÓNOMA][Self-study] 7
Teaching period: Weeks 10-11

Unit 5 (de 5): Social and economic networks
Activities Hours
Class Attendance (theory) [PRESENCIAL][Combination of methods] 10
Class Attendance (practical) [PRESENCIAL][Combination of methods] 1
Writing of reports or projects [AUTÓNOMA][Individual presentation of projects and reports] 14
Study and Exam Preparation [AUTÓNOMA][Self-study] 14
Teaching period: Weeks 12-15

Global activity
Activities hours
General comments about the planning: Teaching period may vary due to unexpected circumstances.
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
Juan A. Aledo, S. Martínez and Jose C. Valverde Parallel Dynamical Systems over Graphs and related topics: A survey 2015 http://dx.doi.org/10.1155/2015/594294  
R. Rodeva Algebraic and Discrete Mathematical Methods for Modern Biology Academic Press 2015  
E. Bujalance y otros Elementos de matemática discreta Sanz y Torres 84-96094-61-8 2005 Ficha de la biblioteca
J.A. Aledo, J. Penabad, J.C. Valverde y J.J. Villaverde Ejercicios de Álgebra y Matemática Discreta I Alpeviva 2001  
J.A. Aledo, J. Penabad, J.C. Valverde y J.J. Villaverde Álgebra y Matemática Discreta Alpeviva 2002  
Jordán Lluch, Cristina Introducción a la teoría de grafos y sus algoritmos Reverté Universidad Politécnica de Valencia 84-7721-438-7 1996 Ficha de la biblioteca
K. Erciyes Complex networks. An algorithmic perspective CRC Press 978-1-4665-7167-9 2015  
Ricardo Vicente Solé, Susanna C. Manrubia Orden y caos en sistemas complejos, Volumen 2 Univ. Politèc. de Catalunya 84-8301-431-9 2009  



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