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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.
• 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.
• Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo.
|1.||Monday, 23.02.2015||Introduction to Complex Network Analysis.||slides||Reading: Chapter 1, 2 of Kleinberg's book and Chapter 1 of Barabasi's book.|
|2.||Monday, 02.03.2015||Basic network measures: degree, distance, clustering||Reading: Chapter 1, 2 of Barabasi's book.|
|3.||Thursday, 05.03.2015||Basic network measures: degree, distance, clustering||slides|
|4.||Monday, 09.03.2015||Random graphs and real networks||slides Random Networks - Barabasi||Reading: Chapter 3 of Barabasi's book|
|5.||Thursday, 12.03.2015||Random graphs and real networks||Random Networks - Barabasi|
|6.||Monday, 16.03.2015||Scale free networks||Scale free networks - Barabasi||Reading: Chapter 4 of Barabasi's book|
|7.||Thursday, 19.03.2015||Scale free networks||slides|
|8.||Monday, 23.03.2015||Small world, Strength of weak ties||slides||Reading: Chapter 3 of Kleinberg's book, Milgram's small world experiment, Watts' email experiment, Leskovec's IM experiment, Granovetter's Strength of Weak Ties theory, Onnela et al.'s Strength of Weak Ties experiment|
|9.||Thursday, 26.03.2015||Centrality measures||slides|
|10.||Monday, 30.03.2015||Network analytics tools (Cytoscape, Gephi, NetworkX)||Guest lecturer: Giulio Rossetti|
|11.||Monday, 20.04.2015||Network models: Small World model and Barabasi-Albert model (Preferential attachment)||slides Small World Model slides Barabasi Albert Model||Read Chapters 4 and 5 of Barabasi's book. Read original papers of Watts-Strogatz model and Barabasi-Albert model|
|12.||Thursday, 23.04.2015||Network robustness to failures and attacks||Reading: Chapter 8 of Barabasi's book|
|13.||Monday, 27.04.2015||Community discovery||slides||Guest lecturer: Giulio Rossetti|
|14.||Thursday, 30.04.2015||Link prediction||Guest lecturer: Luca Pappalardo|
|15.||Monday, 04.05.2015||Student Q&A for Mid Term Project|
|16.||Thursday, 07.05.2015||Diffusion, Spreading & Epidemics: introduction||slides||Reading: Chapter 16 of Kleinberg's book|
|Monday, 11.05.2015||BI Seminar: Marketing plan in 7 steps||Lecturer: Maurizio Fionda (Aula Seminari Est)|
|17.||Thursday, 14.05.2015||Diffusion, Spreading & Epidemics: Decision Models||slides||Reading: Chapter 19 of Kleinberg's book. Bryce Ryan and Neal C. Gross. The diffusion of hybrid seed corn in two Iowa communities|
|18.||Monday, 18.05.2015||Diffusion, Spreading & Epidemics: Decision Models||Lezione cancellata per motivi di salute del docente|
|19.||Thursday, 21.05.2015||Diffusion, Spreading & Epidemics: SIS, SIR models and networks|
|20.||Monday, 25.05.2015||Network effects: Schelling's segregation model|
|21.||Thursday, 28.05.2015||Student Q&A for Final Project. PhD students presentations|