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Social and Information Networks
- Dino Pedreschi Università di Pisa, Knowledge Discovery and Data Mining Lab pedre [at] di [dot] unipi [dot] it
News
- E' online il sito del corso
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 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.
Syllabus
- Graph theory and social networks
- Graphs
- Social, information, biological and technological networks
- Strong and weak ties
- Networks in their surrounding context
- The World Wide Web
- The structure of the Web
- Link analysis and Web search
- Web mining e sponsored search markets
- Network dynamics
- Information cascades
- Power laws and rich-get-richer phenomena
- The small-world phenomenon
- Epidemics
Orario delle lezioni
- Venerdi 14:30 - 17:30
Materiale didattico
- Lucidi dei docenti, resi disponibili attraverso questo wiki (sezione Calendario delle lezioni).
- M. E. J. Newman: The structure and function of complex networks, SIAM Review, Vol. 45, p. 167-256, 2003. (download pdf)
- David Easley, Jon Kleinberg: Networks, Crowds, and Markets. http://www.cs.cornell.edu/home/kleinber/networks-book/
- Anand Rajaraman, Jeffrey D. Ullman, Mining of Massive Datasets. http://infolab.stanford.edu/~ullman/pub/book.pdf
- Testo di consultazione1. : Duncan J. Watts. Six Degrees: The Science of a Connected Age. Norton, New York, 2003.
- Testo di consultazione2. :A.-L. Barabasi. Linked. PLUME, Penguin Group, 2002.
Calendario delle lezioni
| Giorno | Argomento | Lucidi | Docente | |
|---|---|---|---|---|
| 1. | Ven 15.04.2011 | Motivazioni all'Analisi di Reti Sociali, Misure di base su reti reali | sna.giannotti.1.ppt.pdf sna.giannotti.2.ppt.pdf lezione_cytoscape.pdf | |
| 2. | Ven 29.04.2011 | Reti sociali: weak and strong ties, Misure di centralità. Un tool di analisi per reti | 1: pnas-2007-onnela-7332-6.pdf 2: leskovec-im.pdf 3: granstrengthweakties.pdf 4: watts-smallworld2003.pdf 5: travers69smallworld.pdfsna.giannotti.3.pdf, | |
| 3. | Ven 13.05.2011 | Community Discovery, Diffusione di informazione su reti | Lezione: diffusion2.pdf. Articoli da leggere: 1: wanggonzalezhidalgobarabasi_science_2009_sm.pdf, 2: christakis_dynamicspreadhappiness.pdf, 3: viral.pdf, 4: vespignani.pdf, 5: 20100801-coscia-communitydiscoveryreview.pdf | |
| 4. | Ven 20.05.2011 | Modelli generativi delle reti: random, small world, preferential attachment. | ||
| 5. | Ven 27.05.2011 | Community Discovery, Link prediction e evoluzione delle reti | lezione: netevo.pdf, articoli da leggere: 1: 10.1.1.84.2158.pdf(Pannatini), 2: kdd2010.pdf(Daidone), 3: kddlab-ccnr-kdd2011.pdf(Orsini), 4: knowledge_discovery_from_twitter.pdf(Tiloca) | |
| 6. | Ven 03.06.2011 | Da definire |
Modalità di esame
Progetto: una delle modalità di esame è lo svolgimento del progetto che può essere trovato qui: Esercizio 1.
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