NETWORK ANALYTICS

Course objectives

General objectives The primary educational objective of the course is students' learning of the main problems that can be represented on networks (Networks) and quantitative methods of analysis and optimization (Analytics). Students must also be able to correctly use, for decision-making and management purposes, computer tools to analyze data generated by problems on the network, for network optimization, the analysis of complex networks, the generation and simulation of networks. Specific objectives a) Knowledge and ability to understand After attending the course, the main problems on networks (representations, mathematical formulations, main network metrics, parameters and network performance) and the main analytical methods to be used to solve such problems (for example: algorithms, mathematical models) graph theory). b) Ability to apply knowledge and understanding The problems are formalized in the realm of problems. The most appropriate quantitative method, experimenting with the effectiveness of the problem. c) Autonomy of judgment Students develop critical skills through the application of modeling, analysis and optimization to a broad set of network problems. They also develop the critical sense through the comparison between alternative solutions to the same problem using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. d) Communication skills Students, through the study and the carrying out of the practical exercises, acquire the technical-scientific language of the course, which should be used in the oral tests. Communication skills are also developed through group activities. e) Learning ability Students who pass the exam have methods of analysis and optimization on networks that allow them to face, decision-making and optimization of complex systems and networks.

Channel 1
PAOLO DELL'OLMO Lecturers' profile

Program - Frequency - Exams

Course program
The lessons are divided into four main parts Part 1: Network Science (about 18 hours). Arguments: fundamentals of graph theory, measures and structure of networks, methods of network analysis, algorithms and mathematical models of optimization, generative methods and dynamic processes on networks. Part 2: Code Networks (about 18 hours). Arguments: waiting file theory, Markov chains, Kolmogorov equations, main analytical models in stationary conditions, queue networks, network performance measures. Part 3: Simulation of network processes (about 18 hours). Topics: discrete simulation on the network, analysis of input and output data, non-stationary processes, generation processes, propagation and diffusion. Part 4: Analysis of Networks, Optimization and Experimentation (about 18 hours).
Prerequisites
Per affrontare i contenuti dell’insegnamento è indispensabile possedere le nozioni di base dell'Analisi matematica e dell’Algebra lineare.
Books
Teaching material made available at sito elearning2 Sapienza. Mark Newman Networks: An Introduction, Oxford University Press, 2nd Edition (2018). Albert-Laszlo Barabasi. (2016) Network Science, anche disponibile gratuitamente nella versione on-line al sito http://networksciencebook.com/line. M. Laguna, J. Marklund: Business Process Modelling, simulation, and Design, Pearson Education Inc., Prentice Hall, 2005. https://networkx.github.io/documentation/stable/_downloads/networkx_reference.pdf
Teaching mode
The course includes face-to-face lectures in which the course contents are exposed using the blackboard, power point presentations, examples and case studies to be carried out interactively. The contents presented are uploaded to the Moodle e-learning platform after the lesson. Students are invited to upload the results of the exercises to the platform and a selection of these is made public for all students (with prior consent). The course includes the use of the computer lab where students can work independently and experiment with analysis models and algorithms on real networks.
Frequency
The course includes face-to-face lectures in which the course contents are exposed using the blackboard, power point presentations, examples and case studies to be carried out interactively. The contents presented are uploaded to the Moodle e-learning platform after the lesson. Students are invited to upload the results of the exercises to the platform and a selection of these is made public for all students (with prior consent). The course includes the use of the computer lab where students can work independently and experiment with analysis models and algorithms on real networks.
Exam mode
The exam consists of a written test with some exercises inspired by those carried out during the course and some open-ended questions on the topics covered in class.The project can also be carried out in small groups of two or three people.
Bibliography
Teaching material made available at sito elearning2 Sapienza. Mark Newman Networks: An Introduction, Oxford University Press, 2nd Edition (2018). Albert-Laszlo Barabasi. (2016) Network Science, anche disponibile gratuitamente nella versione on-line al sito http://networksciencebook.com/line. M. Laguna, J. Marklund: Business Process Modelling, simulation, and Design, Pearson Education Inc., Prentice Hall, 2005. https://networkx.github.io/documentation/stable/_downloads/networkx_reference.pdf
Lesson mode
The course includes face-to-face lectures in which the course contents are exposed using the blackboard, power point presentations, examples and case studies to be carried out interactively. The contents presented are uploaded to the Moodle e-learning platform after the lesson. Students are invited to upload the results of the exercises to the platform and a selection of these is made public for all students (with prior consent). The course includes the use of the computer lab where students can work independently and experiment with analysis models and algorithms on real networks.
  • Lesson code1055807
  • Academic year2025/2026
  • CourseStatistics for management
  • CurriculumSingle curriculum
  • Year2nd year
  • Semester2nd semester
  • SSDMAT/09
  • CFU9