====== Social Network Analysis - Crash Course at Universitat Pompeu Fabra - Barcelona - May 2016 ======

  * **Dino Pedreschi** Università di Pisa, Knowledge Discovery and Data Mining Lab [[pedre@di.unipi.it]]



===== Schedule =====

  * **Monday, May 23: h 14:00 - 19:00**
  * **Tuesday, May 24: h 10:00 - 13:00, h 15:00-17:00**
  * **Wednesday, May 25: h 09:00 - 14:00**

====== 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.

• Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo.


====== Textbooks ======

  * **David Easley, Jon Kleinberg: Networks, Crowds, and Markets (2010) [[http://www.cs.cornell.edu/home/kleinber/networks-book/]]**

  * **Albert-Laszlo Barabasi. Network Science (2016) [[http://barabasi.com/book/network-science]]**



====== Software ======

  * Visual Analytics: [[http://www.cytoscape.org/|Cytoscape]], [[http://gephi.github.io/|Gephi]]
  * Network Simulation: [[https://ccl.northwestern.edu/netlogo/|NetLogo]]
  * Python 2.7: [[https://www.python.org/|Guide and Download]], [[https://www.jetbrains.com/pycharm/|PyCharm]], [[http://networkx.github.io/|NetworkX]]



====== Slides ======

  * Lecture 1: The architecture of complexity {{:wma:pedreschi.sna.upf.may2016.part1.pdf|pdf}}
  * Lecture 2: Centrality, communities, strength of weak ties {{:wma:pedreschi.sna.upf.may2016.part2.pdf|pdf}}
  * Lecture 3: Diffusion and spreading {{:wma:pedreschi.sna.upf.may2016.part3.pdf|pdf}}

====== Network data ======

  * {{:wma:networkdatasets.zip|Example networks}}
  * Online repository of network datasets [[http://networkrepository.com/ 
]]

====== Link to Social Network Analysis course at University of Pisa ======

[[http://didawiki.di.unipi.it/doku.php/wma/start | link]]