Suggested (optional) prerequisites:

• data mining
• python programming

The course is organized in three parts.

1st part: The Architecture of Complex Networks

• Networks and Graphs
• Random graphs
• It’s a Small world
• Scale Free Networks
• Centrality & Assortative Mixing
• Tie Strength & Resilience
• High-order Network Analysis

2nd part: The Dynamics of Complex Networks

• Community Discovery
• Dynamic of networks
• Link Prediction
• Dynamic Community Discovery
• Diffusion: Decision based models
• Diffusion: Epidemics
• Diffusion: Opinion Dynamics

3rd part: Case Studies

• External Guest Lecture (TBD)
• Polarization, Echo Chambers and Online Debates
• SNA @ KDD Laboratory

NB: Hands-on tutorials covering the course themes are provided in Python and through Gephi/Cytoscape.

Reference books:

• D. Easley, J. Kleinberg: Networks, Crowds, and Markets.
• A. L. Barabasi: Network Science 
• M. Coscia: The Atlas for Aspiring Network Scientists

Additional resources (i.e., research papers) will be specified by the instructors during each lecture.

For non-attending students the same course program and assesment methods applies.

The final exam comprises an individual written test and one group project to discuss orally. 

• The written test can be replaced by successfully addressing two midterms.
• Written test(s) cover the course's theoretical grounds, proposing practical exercises and open questions.
• The group project (up to 3 members) involves data collection, network construction and characterization, deployment of a limited set of standard network tasks, and formulating and addressing an open research question on the collected data. All results need to be summarized and commented on in a written report.