Weekly outline

  • Announcements

    • In this document you can find the results of the student survey: use them to form groups.

      • Groups can be composed of up to 4 members.
      • Groups (whenever possible) should be heterogeneous w.r.t. the MS degrees of their components (it's a Social Network Analysis course... socialize!)
      • Once a group is formed specify its id in the spreadsheet's dedicated column for each of its components. 
      • Finally, send an email to prof. Rossetti (cc Morini) with the final group members.
  • Introduction


    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. Simulation of network diffusion processes with NDlib.

  • 2021 schedule and instructors


    Monday, h 11:00 - 13:00, online
    Thursday, h 11:00 - 13:00, online


    Dino Pedreschi | Università di Pisa | Knowledge Discovery and Data Mining Lab | dino.pedreschi@unipi.it  
    Giulio Rossetti | ISTI-CNR | Knowledge Discovery and Data Mining Lab | giulio.rossetti@isti.cnr.it | Office @CNR: room C37, 1st floor, gate 20
    Virginia Morini | ISTI-CNR | Knowledge Discovery and Data Mining Lab | virginia.morini@isti.cnr.it 

  • Textbooks and material



    Additional readings (mostly scientific papers) are listed on the course slides. Among them,

    • M. E. J. Newman. "The structure and function of complex networks." SIAM Review, Vol. 45, p. 167-256, 2003. 
    • A.-L. Barabasi. "Linked". PLUME, Penguin Group, 2002.
    • Duncan J. Watts.  "Six Degrees: The Science of a Connected Age." Norton, New York,  2003.
    • Anand Rajaraman, Jeffrey D. Ullman. "Mining of Massive Datasets".

    Past Exams, Slides, Python Notebooks:

    Exams Dates:

    The final project must be submitted within the following deadlines:

    • June and July, 2021, TBD

    Oral exam dates will be fixed after each deadline.

    NB 1: each student of the group must register for the exam at least 5 days before the specified deadlines.

    NB 2: the report, in pdf format, must be submitted via email to all professors and assistants (specifying in the subject [SNA-Project])

  • Lecture calendar

    Lectures take place online on Microsoft Teams 668AA 20/21 - SOCIAL NETWORK ANALYSIS. (For the setup guide refer to the UNIPI documentation.)

    DateTopicLearning materialHomework


     Introduction to Complex Network Analysis

    video 2021

     Chapter 1, 2 of Kleinberg's book and Chapter 1 of Barabasi's book.


     Graphs and networks. Basic measures

    video 2021

    Reading: Chapter 2 of Barabasi's book.



     Graphs and networks. Basic measures.

    video 2021
    Reading: Chapter 2 of Barabasi's book.


     Random networks

    video 2021

     Reading: Chapter 3 of Barabasi's book.


     It's a small world!

    video 2021

     Reading: Chapter 20 of Kleinberg’s book.
     Milgram's 6 degrees paper
     Watts-Strogatz's Small World paper.




     Scale-free networks slides 
    video 2021 (pt1)
    video 2021 (pt2)
     Reading: Chapters 4 & 5 of Barabasi's book
     Barabasi-Albert Preferential Attachment model.


     Centrality & Assortative Mixing

    video 2021

      Reading: Chapter 3 & 4 of Kleinberg's book



     Tie Strength & Resilience

    video 2021

      Reading: Chapter 8 of Barabasi's book and Chapter 3 of Kleinberg's book

     9 17.03.2020 Gephi & Cytoscape Tutorialvideo
    video 2021


     10 22.03.2021 Community Discoveryslides
    video 2021

     Reading: Chapter 9 of Barabasi's book
     Survey: Community Detection in Graphs
     Algorithm specific papers as reported in the slides

     11 24.03.2021 Dynamic Of Networksslides
     Survey: Temporal Networks
     Stream graphs and link streams for the modeling of interactions over time

     12 29.03.2021 Link Prediction

     Survey: The link‐prediction problem for social networks.
     13 31.03.2021  Dynamic Community Discoveryslides
     Challenges in community discovery on temporal networks
     Survey: Community Discovery in Dynamic Networks: a Survey



     Diffusion: Decision-based models
    Chapter 19 of Kleinberg's book
     Threshold models of collective behavior
     Rogers, E. M. “Diffusion of innovations”



     Diffusion: Epidemics*


      Reading: Chapter 21 of Kleinberg's book and Chapter 10 of Barabasi's book

     21.04.2021 Diffusion: Opinion Dynamics 

     Opinion dynamics: models, extensions and external effects.
     Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model


     Case Studies: Complex Network Analysis @KDD Lab 



     19 03.05.2021 TBD   
     20 05.05.2021 TBD  

  • Software, tutorials and datasets

    Visual Tools: 

    Python >=3.7

    Lectures' Notebooks:

    Network Data Repository:

  • Call For Thesis in Network Analysis

    Have you enjoyed the SNA course so much that you are considering a thesis on related subjects? Great!

    Here a few ideas we would like to work on... of course you can also propose something new and completely different!

    • NDlib: diffusion model comparison framework
    • NDlib: modeling competing diffusion processes
    • NDlib: definition of novel diffusion models tailored for specific scenarios (e.g., fake news, opinion dynamics...)
    • COVID19 modelling

    • CDlib: comparative analysis of Community Discovery algorithms
    • CDlib: definition of novel community discovery approaches for dynamic/multiplex networks
    • CDlib-viz: a visual framework for the analysis of community partitions 

    • XAI: Explaining Community Discovery Algorithms
    • DyNetX: a library for modeling and studying dynamic network topologies
    • Graph Embedding: representing (static|dynamic) networks in low dimensional space to support prediction and clustering
    • High Order Networks: studying and modeling high-order temporal networks

    • Country-wide Agent-Based simulation systems
    • Network Medicine applications
    • Semantic Network Analysis
    • Scholarly Data Analysis
    • Migration through the lenses of Social Media Platform
    • Fake News, Echo Chambers, Polarization
    • ...

    A thesis can focus either on the definition of a new model/algorithm or on the usage of complex network analysis methodologies as tools for studying specific phenomena.

    Contact Dino Pedreschi and Giulio Rossetti for more details.