wma:acm-athens-july2017
Differenze
Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.
| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente | ||
| wma:acm-athens-july2017 [14/07/2017 alle 15:29 (7 anni fa)] – [Slides] Dino Pedreschi | wma:acm-athens-july2017 [15/07/2017 alle 09:53 (7 anni fa)] (versione attuale) – [Lecture slides] Dino Pedreschi | ||
|---|---|---|---|
| Linea 11: | Linea 11: | ||
| ====== Social Network Analytics ====== | ====== Social Network Analytics ====== | ||
| - | ====== Goals ====== | + | ===== 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. | 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 | + | ===== Syllabus ===== |
| • Big graph data and social, information, | • Big graph data and social, information, | ||
| Linea 26: | Linea 26: | ||
| - | ====== Textbooks | + | ===== Textbooks ===== |
| * **David Easley, Jon Kleinberg: Networks, Crowds, and Markets (2010) [[http:// | * **David Easley, Jon Kleinberg: Networks, Crowds, and Markets (2010) [[http:// | ||
| Linea 34: | Linea 34: | ||
| - | ====== Software | + | ===== Software ===== |
| * Visual Analytics: [[http:// | * Visual Analytics: [[http:// | ||
| Linea 42: | Linea 42: | ||
| - | ====== Lecture slides | + | ===== Lecture slides ===== |
| * Lecture 1: The architecture of complexity {{ : | * Lecture 1: The architecture of complexity {{ : | ||
| - | * Lecture 2: The power of complex networks {{ : | + | * Lecture 2: The power of complex networks {{ : |
| - | ====== Network data ====== | + | ===== Network data ===== |
| * {{: | * {{: | ||
| Linea 54: | Linea 54: | ||
| ]] | ]] | ||
| - | ====== Link to Social Network Analysis course at University of Pisa ====== | + | ===== Networks for hands-on session ===== |
| + | |||
| + | * {{ : | ||
| + | * {{ : | ||
| + | ===== Link to Social Network Analysis course at University of Pisa ===== | ||
| [[http:// | [[http:// | ||
| Linea 69: | Linea 73: | ||
| Often, the achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. Examples include mobile phone call records, originally collected by telecom operators for billing and operations, used for accurate and timely demography and human mobility analysis at country orregional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity, secure data, engage users, avoid discrimination and misuse, account for transparency and fair use — to the purpose of seizing the opportunities of data science while controlling the associated risks. This is the focus of my lecture. | Often, the achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. Examples include mobile phone call records, originally collected by telecom operators for billing and operations, used for accurate and timely demography and human mobility analysis at country orregional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity, secure data, engage users, avoid discrimination and misuse, account for transparency and fair use — to the purpose of seizing the opportunities of data science while controlling the associated risks. This is the focus of my lecture. | ||
| - | ====== Syllabus | + | ===== Syllabus ===== |
| * Fairness, Accountability, | * Fairness, Accountability, | ||
| Linea 78: | Linea 82: | ||
| * Algorithmic bias and ethical challenges of machine learning | * Algorithmic bias and ethical challenges of machine learning | ||
| * Discrimination-aware data mining | * Discrimination-aware data mining | ||
| + | |||
| + | ===== Lecture slides ===== | ||
| + | |||
| + | * Lecture 3: Data ethics & privacy-preserving analytics {{ : | ||
wma/acm-athens-july2017.1500046196.txt.gz · Ultima modifica: 14/07/2017 alle 15:29 (7 anni fa) da Dino Pedreschi