Corso di Basi di Dati A, 2020/2021

The course will, through presentation of actual working cases and project work, enable the student to produce and/or appropriately use software tools for the support to complex decisions (mainly at the corporate/industrial level), in particular those based on mathematical optimization techniques. The course is focused on practical aspects of these tools (modeling languages and systems, solvers and interfaces, algorithmic parameters, ...) and has a strong project aspect in order to familiarize the students, in particular, with the specific computer science aspects of these activities. However, since these tools are based on complex algorithmic schemes and rigorously defined mathematical properties, it is at the same time necessary to provide the students with appropriate consciousness of these foundational aspects, in particular whenever this is necessary to better understand their use or design more efficient and effective approaches; a particularly relevant case is that of handling the issue of uncertainty in the data of the problem.

The course will, through presentation of actual working cases and project work, enable the student to produce and/or appropriately use software tools for the support to complex decisions (mainly at the corporate/industrial level), in particular those based on mathematical optimization techniques. The course is focussed on practical aspects of these tools (modeling languages and systems, solvers and interfaces, algorithmic parameters, ...) and has a strong project aspect in order to familiarize the students, in particular, with the specific computer science aspects of these activities. However, since these tools are based on complex algorithmic schemes and rigorously defined mathematical properties, it is at the same time necessary to provide the students with appropriate consciousness of these foundational aspects, in particular whenever this is necessary to better understand their use or design more efficient and effective approaches; a particularly relevant case is that of handling the issue of uncertainty in the data of the problem.

Goals

Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

This crash course is an introduction to the analysis of complex networks, made possible by the availability of big data, with a special focus on the social network and its structure and function. Drawing on ideas from computing and information science, complex systems, mathematic and statistical modelling, economics and sociology, this lecture sketchily describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

Syllabus

  • Big graph data and social, information, biological and technological networks
  • The architecture of complexity and how real networks differ from random networks: node degree and long tails, social distance and small worlds, clustering and triadic closure. Comparing real networks and random graphs. The main models of network science: small world and preferential attachment.
  • Strong and weak ties, community structure and long-range bridges. Robustness of networks to failures and attacks. Cascades and spreading. Network models for diffusion and epidemics. The strength of weak ties for the diffusion of information. The strength of strong ties for the diffusion of innovation. Models of opinion dynamics.
  • Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo. Simulation of network diffusion processes with NDlib.

2019 schedule and instructors

  • Monday, h 11:00 - 13:00, Aula N1
  • Tuesday, h 16:00 - 18:00, Aula N1

Dino Pedreschi | Università di Pisa  |  Knowledge Discovery and Data Mining Lab  |  dino.pedreschi@unipi.it 

Giulio Rossetti | ISTI-CNR  |  Knowledge Discovery and Data Mining Lab  |   giulio.rossetti@isti.cnr.it 


Goals

Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

This crash course is an introduction to the analysis of complex networks, made possible by the availability of big data, with a special focus on the social network and its structure and function. Drawing on ideas from computing and information science, complex systems, mathematic and statistical modelling, economics and sociology, this lecture sketchily describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

Syllabus


  • Big graph data and social, information, biological and technological networks
  • The architecture of complexity and how real networks differ from random networks: node degree and long tails, social distance and small worlds, clustering and triadic closure. Comparing real networks and random graphs. The main models of network science: small world and preferential attachment.
  • Strong and weak ties, community structure and long-range bridges. Robustness of networks to failures and attacks. Cascades and spreading. Network models for diffusion and epidemics. The strength of weak ties for the diffusion of information. The strength of strong ties for the diffusion of innovation. Models of opinion dynamics.
  • Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo. Simulation of network diffusion processes with NDlib.

2017 schedule and instructors

  • Monday, h 11:00 - 13:00, Aula C1
  • Tuesday, h 16:00 - 18:00, Aula N1

Dino Pedreschi | Università di Pisa  |  Knowledge Discovery and Data Mining Lab  |  dino.pedreschi@unipi.it 

Giulio Rossetti | ISTI-CNR  |  Knowledge Discovery and Data Mining Lab  |   giulio.rossetti@isti.cnr.it 

Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else.

This short course is an introduction to the analysis of complex networks, with a special focus on social networks and the Web - its structure and function, and how it can be exploited to search for information. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, the course describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.