Social Network Analysis 2025

Section outline

• Select section News and Updates

  News and Updates

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  • Select activity News

    

    News Forum

    Updates on the course schedule (e.g., canceled/postponed/online lectures) and on written exams results.
  • Select activity Student survey

    

    Student survey URL

    SNA is an interdisciplinary course: please, fill the survey to allow us better capture this year students' demographic 

  • Select activity Official SNA Telegram Channel

    

    Official SNA Telegram Channel URL

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• Select section 2025 schedule and instructors

  2025 schedule and instructors

  Schedule

  • Monday, h 09:00 - 11:00 (Fib C1)
  • Thursday, h 9:00 - 11:00 (Fib C)

  Contacts

  • Prof. Dino Pedreschi | Università di Pisa | dino.pedreschi@unipi.it
  • Prof. Giulio Rossetti | CNR-ISTI | giulio.rossetti@isti.cnr.it
  • Dott. Erica Cau | Università di Pisa | erica.cau@phd.unipi.it
• Select section Introduction

  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.

  
  
• Select section Textbooks and material

  Textbooks and material

  Textbooks:

  • David Easley, Jon Kleinberg: Networks, Crowds, and Markets. 
  • Albert-Laszlo Barabasi. Network Science
  • Michele Coscia. The Atlas for Aspiring Network Scientists
  • Dmitry Zinoviev: Complex Network Analysis in Python 

  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:

  • Course GitHub Organization
• Select section Lecture calendar

  Lecture calendar

  Lectures take place in presence and online on MS Teams(Only for PhD students or master students with a justified reason for remote attendance.)

   

  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. 

  	Date	Topic	Slides	Materials
  1		Introduction to Complex Network Analysis	Slides  Recordings	Reading: Chapter 1, 2 of Kleinberg's book and Chapter 1 of Barabasi's book.
  2		Graphs and networks. Basic measures	Slides  Notebook  Recordings 1  Recordings 2	Reading: Chapter 2 of Barabasi's book.
  3		Random networks	Slides  Notebook  Recordings	Reading: Chapter 3 of Barabasi's book.
  4		It's a small world!	Slides  Notebook  Recordings	Reading: Chapter 20 of Kleinberg’s book.    Papers:  Milgram's 6 degrees paper. (suggested)  Watts-Strogatz's Small World paper. (suggested)
  5		Scale-free networks	Slides  Notebook  Recordings	Reading: Chapters 4 & 5 of Barabasi's book    Papers:  Barabasi-Albert Preferential Attachment model. (suggested)
  6		Centrality & Assortative Mixing	Slides  Notebook	Reading: Chapter 3 & 4 of Kleinberg's book
  7		Tie Strength & Resilience	Slides  Notebook	Reading: Chapter 8 of Barabasi's book and Chapter 3 of Kleinberg's book
  8		High-order network analysis	Slides  Notebook	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)
  9		Exercise for the 1st midterm
  10		Gephi & Cytoscape Tutorial
  11		Community Discovery	Slides  Notebook	Reading: Chapter 9 of Barabasi's book  Survey:   Community Detection in Graphs (suggested)    Papers:  Algorithm specific papers as reported in the slides (suggested)
  12		1st MidTerm
  13		Dynamic Of Networks	Slides  Notebook	Survey:   Temporal Networks  Papers:  Stream graphs and link streams for the modeling of interactions over time (suggested)
  14		Dynamic Community Discovery	Slides  Notebook	Papers:  Challenges in community discovery on temporal networks    Survey:   Community Discovery in Dynamic Networks: a Survey (appendix not needed)
  15		Link Prediction	Slides  Notebook	Survey:   The link‐prediction problem for social networks.
  16	14/4/25	Diffusion: Decision-based models	Slides  Notebook	Reading: Chapter 19 of Kleinberg's book   Papers:   Threshold models of collective behavior (suggested)    Book:  Rogers, E. M. “Diffusion of innovations” (suggested)
  17	17/4/25	Diffusion: Epidemics	Slides  Notebook	Reading: Chapter 21 of Kleinberg's book and Chapter 10 of Barabasi's book
  18	28/4/25	Diffusion: Opinion Dynamics	Slides  Notebook	Papers:  Opinion dynamics: models, extensions and external effects.  Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model (suggested)
  19	5/5/25	Case Studies
  20	8/5/25	PhD lectures (TBD)
  21	12/5/25	Exercise for the 2nd midterm
  22	15/5/25	2nd Midterm

  
  
  
  
• Select section Software, tutorials and datasets

  Software, tutorials and datasets

  Visual Tools: 

  • Cytoscape 
  • Gephi
  • Datasets (used in the tutorial)

   

  Python >=3.10: 

  • Anaconda (Jupyter notebooks included)
  • PyCharm IDE

  • Network Science Libraries
  • Network modeling: NetworkX
  • Dynamic Network modeling: DyNetX
  • Community Discovery: CDlib
  • Diffusion and Epidemics: NDlib
  • Link Prediction: linkpred

  • [NEW] SNA Laboratory
    • Docker image with all python libraries preinstalled: SNAlab

   

  Lectures' Notebooks:

  • SNA python material (Jupyter notebooks)

   

  Network Data Repository:

  • http://networkrepository.com/
  • https://snap.stanford.edu/data

  
  
  
• Select section Call For Thesis in Network Analysis

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

  • Mental Health in Online Social Media
  • Fake News, Echo Chamber, Polarization
  • Cognitive Network Science
  • Feature-rich network modeling
  • NDlib: modeling competing diffusion processes
  • NDlib: definition of novel diffusion models tailored for specific scenarios (e.g., fake news, opinion dynamics...)
  • CDlib: a comparative analysis of Community Discovery algorithms
  • CDlib: definition of novel community discovery approaches for dynamic/multiplex/feature-rich networks
  • 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

  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.
  

  
  

  Past Thesis example:

  
  

  • Data Warehouse For Reddit Data And Its Usage
    
  • Modeling peer-pressure: a data-driven estimate of open-mindedness from high-order online political discussions
    
  • Echo chamber and Political Polarization: A time- and linguistic-aware analysis of online polarized discussions
  • Towards Attributed Stream-Hypernetwork Analysis: Structure, Features and Dynamics of Complex Social Systems
  • Can language be modelled through Network Science? A Textual Forma Mentis Network case study
  • Filter Bubbles in the Italian Twittersphere: a data-driven analysis
  • A tale of two OSNs: online political debate around Capitol Hill assault on Twitter and Parler
  • Reasoning, Machine Learning and Network Science: a comprehensive approach to Knowledge Graphs interlinking
  • Fiori, reti e frattali: un approccio nature-inspired per l'analisi di network.
  • Scholarly Career Paths: Brain Drain and Exterophily
  • Polarizzazione Politica & Echo Chamber: una metodologia per l'identificazione e analisi su Reddit
  • Modeling Algorithmic Bias in Opinion Dynamics: simplicial complexes, and evolving network topologies
  • Modeling Human Mobility considering Spatial, Temporal and Social Dimensions
  • Bounded confidence, Stubborness e Peer Pressure: simulare l'adozione di fake news tramite modelli di opinion dynamics
  • La struttura complessa degli spazi semantici: un approccio guidato dalla network science
  • "It's a long way to the top" Predicting Success via Innovators'adoptions
  • Supervised Link Prediction su Reti Sociali

  
  

  Interested? Contact Giulio Rossetti for more details.

  
  
• Select section Articles from former students' Final Projects

  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)

  
  

  2023
  

  • Giulio Cordova, Luca Palla, Martina Sustrico and Giulio Rossetti.
    "I Like You if You Are Like Me: How the Italians’ Opinion on Twitter About Migrants Changed After the 2022 Russo-Ukrainian Conflict"
    Complex Networks 2023. (Full paper)