Weekly outline

  • Announcments

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  • 2022 schedule and instructors

    Schedule

    • Monday, h 9:00 - 10:45 (Fib C)
    • Wednesday, h 9:00 - 10:45 (Fib L1)

    Contacts

    • Dino Pedreschi | Università di Pisa | dino.pedreschi@unipi.it
    • Giulio Rossetti | ISTI-CNR | giulio.rossetti@isti.cnr.it
    • Virginia Morini | Università di Pisa | virginia.morini@phd.unipi.it

    Written Exam Sessions

    • 10/6 at 9, room C1
    • 4/7 at 14, room C1

  • Introduction

    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 modeling, 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

    • Real-world network characterization:
      • 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.
      • Assortativity and homophilic behaviors.
      • Strong and weak ties, community structure, and long-range bridges. 
      • Network beyond pairwise interactions: high-order network modeling.
    • Applications:
      • Robustness of networks to failures and attacks. 
      • Dynamic Network modeling.
      • Dynamic Community Discovery.
      • Link Prediction
      • Cascades and spreading. 
      • Network models for opinion dynamics and epidemics. 

    Hands-On
    • Practical network analytics with Cytoscape and Gephi. 
    • Simulation of network processes with NetLogo. 
    • Advanced network analysis and modeling with Python.

  • Textbooks and material

    Textbooks:


    Reading:

    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:


  • Lecture calendar

    Lectures take place both in presence and online (on Microsoft Teams 668AA 21/22 - SOCIAL NETWORK ANALYSIS - For the setup guide refer to the UNIPI documentation)
    All lectures will be recorded and made available along with the slides/notebooks used in class (you can find them under the "Files" tab in the Team channel).

    For each lecture are reported the book chapters/papers to be studied: while some of them are mandatory (i.e., most book chapters and surveys) others are suggested readings. 


    		DateTopic (and teacher)  Slides/Video   Materials
     
     1

    		 
     14.02.2022
     16.02.2022

    		 
     Introduction to Complex Network Analysis
    (Dino Pedreschi, Giulio Rossetti)


     Slides

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

     
     2

    		 
     21.02.2022 
         		 
     Graphs and networks. Basic measures
    (Dino Pedreschi)


     Slides

    		 
     Reading: Chapter 2 of Barabasi's book.

     
     3

    		 
     23.02.2022
         		 
     Random networks
    (Dino Pedreschi)

    		 Slides
     Reading: Chapter 3 of Barabasi's book.


     4

    		 
     28.02.2022
         		 
     It's a small world!
    (Giulio Rossetti)

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

     
     5

    		 
     2.03.2022

    		 Scale-free networks
    (Dino Pedreschi, Giulio Rossetti)    		 Slides 
     Reading: Chapters 4 & 5 of Barabasi's book
     
     Papers:
     Barabasi-Albert Preferential Attachment model. (suggested)

     
     6

    		 
     7.03.2022

    		 
     Centrality & Assortative Mixing
    (Giulio Rossetti)

    		 Slides
     Reading: Chapter 3 & 4 of Kleinberg's book

     
     7

    		 
     9.03.2022

    		 
     Tie Strength & Resilience
    (Dino Pedreschi, Giulio Rossetti)

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

     8 14.03.2022 Exercise for the 1st midterm
    (Dino Pedreschi, Giulio Rossetti)    		  
     9 16.03.2022Gephi & Cytoscape Tutorial
    (Dino Pedreschi, Giulio Rossetti)
    		   
     
     10 21.03.2022 High-order network analysis
    (Dino Pedreschi, Giulio Rossetti)    		 Slides
     Papers:
     The why, how, and when of representations for complex systems
     Hypernetwork science via high-order hypergraph walks
     Networks beyond pairwise interactions: structure and dynamics (suggested)
     Hypernetwork Science: From Multidimensional Networks to Computational Topology (suggested)

     11 23.03.2022
     28.03.2022     		 Community Discovery
    (Dino Pedreschi, Giulio Rossetti)    		 Slides 
     Reading:     Chapter 9 of Barabasi's book

     Survey: 
     Community Detection in Graphs (suggested)
     
     Papers:
     Algorithm specific papers as reported in the slides (suggested)

     12 30.03.2022 Dynamic Of Networks
    (Giulio Rossetti)    		 Slides 
     Survey: 
     Temporal Networks

     Papers:
     Stream graphs and link streams for the modeling of interactions over time (suggested)

     13 04.04.2022 1st Midterm
    (Dino Pedreschi, Giulio Rossetti)


    		 
     14 06.04.2022 Link Prediction
    (Dino Pedreschi, Giulio Rossetti)    		 Slides  Survey: 
     The link‐prediction problem for social networks.
     15   Dynamic Community Discovery
    (Giulio Rossetti)    		 Slides  
     Papers:
     Challenges in community discovery on temporal networks
     
     Survey: 
     Community Discovery in Dynamic Networks: a Survey (appendix not needed)

     
     16

    		  Diffusion: Decision-based models
    (Dino Pedreschi)
     Slides
         		 
     Reading:     Chapter 19 of Kleinberg's book     

     Papers: 
     Threshold models of collective behavior (suggested)
     
     Book:
     Rogers, E. M. “Diffusion of innovations” (suggested)

      17 
     

    		 Diffusion: Epidemics
    (Dino Pedreschi)    		 Slides

    		 Reading: Chapter 21 of Kleinberg's book and Chapter 10 of Barabasi's book
     
      18


    		 Diffusion: Opinion Dynamics
    (Giulio Rossetti)    		 
     Slides

    		  
     Papers:
     Opinion dynamics: models, extensions and external effects.
     Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model (suggested) 
     
     
     19

    		 
     Applications:
     Cognitive Network Science
     (Guest Lecture by Prof. Stella) 


    		 
     20  
     Applications:
     Echo Chambers, d/misinformation and
     Polluted Information Environments
    (Guest Lecture by Dott. Morini and Dott. Pansanella)
     
    		  
     21 Exercise for the 2nd midterm
    (Dino Pedreschi, Giulio Rossetti)
    		  
     22  2nd Midterm
    (Dino Pedreschi, Giulio Rossetti)    		  



  • Software, tutorials and datasets

    Visual Tools: 


    Python >=3.8: 

    • Network Science Libraries


    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...)
    • 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.



  • Articles from former students' Final Projects

    Since the introduction of the "Open Problem" within the Final Term Assignment, we decided to support students in writing and submitting their first scientific conference contribution (either abstract or full papers). 

    Our preferred submission venue for student contributions is the Complex Network conference.
    We are very proud of the acceptance rate of our students' works (100% so far, also with a couple of awards!) underlying the overall quality of your projects.

    Indeed not all projects can/have to be published: if your analysis is valid and you are interested in such an opportunity we will discuss it after the oral exam.

    2019
    • Arianna Nocente, Jarir Salame Younis, Marco Cozzolino and Giulio Rossetti. 
      "      Does Road Network Topology Affect Real Estate Pricing? The Naples Case Study".
      Complex Networks 2019 (Abstract - Best Poster Award)

    2020
    • Gabriele Pisciotta, Miriana Somenzi, Elisa Barisani and Giulio Rossetti.
      "Sockpuppet Detection: a Telegram case study".
      Complex Networks 2020 (Abstract - Best Presentation Award)
    • Vitalba Macaluso, Clara D'Apoli and Giulio Rosetti.
      "Quarantined world through SoundCloud hashtags network"
      Complex Network 2020. (Abstract)
    • Tommaso Cavalieri, Andrea Fedele, Federica Guiducci, Valentina Olivotto and Giulio Rossetti.
      "A network analysis of personnel exchange and companies’ relevant sector: the LinkedIn case study".
      Complex Networks 2020. (Abstract)

    2021

    • Christian Esposito, Marco Gortan, Lorenzo Testa, Francesca Chiaromonte, Giorgio Fagiolo, Andrea Mina and Giulio Rossetti.
      "Can you always reap what you sow? Network and functional data analysis of VC investments in health-tech companies".
      Complex Networks 2021. (Full paper)
    • Sirio Papa, Beatrice Rosi, Lorenzo Testa, Francesco Vaselli and Giulio Rossetti.
      "Inequality in the menu: How a network of restaurants characterizes social disparities in Boston".
      Complex Networks 2021. (Abstract)

    2022 

    • Andrea Failla, Salvatore Citraro and Giulio Rossetti.
      "Attributed Stream-Hypernetwork analysis: Homophilic Behaviors in Pairwise and Group Political Discussions on Reddit".
      Complex Networks 2022. (Full paper)
    • Chiara Buongiovanni, Roswita Candusso, Giacomo Cerretini, Diego Febbe, Virginia Morini and Giulio Rossetti.
      "Will You Take the Knee? Italian Twitter Echo Chambers' Genesis during EURO 2020".
      Complex Networks 2022. (Full Paper)
    • Andrea Failla, Federico Mazzoni and Salvatore Citraro.
      "      Attribute-aware Community Events in Feature-rich Dynamic Networks".
      Complex Networks 2022. (Abstract)