Weekly outline


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

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 longrange 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.
 Big graph data and social, information, biological and technological networks

Schedule
Monday, h 11:00  13:00, online
Thursday, h 11:00  13:00, onlineContacts
Dino Pedreschi  Università di Pisa  Knowledge Discovery and Data Mining Lab  dino.pedreschi@unipi.it
Giulio Rossetti  ISTICNR  Knowledge Discovery and Data Mining Lab  giulio.rossetti@isti.cnr.it  Office @CNR: room C37, 1st floor, gate 20
Virginia Morini  ISTICNR  Knowledge Discovery and Data Mining Lab  virginia.morini@isti.cnr.it

Textbooks:
 David Easley, Jon Kleinberg: Networks, Crowds, and Markets.
 AlbertLaszlo 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. 167256, 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
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 [SNAProject])

Lectures take place online on Microsoft Teams 668AA 20/21  SOCIAL NETWORK ANALYSIS. (For the setup guide refer to the UNIPI documentation.)
Date Topic Learning material Homework
1
15.02.2021
Introduction to Complex Network Analysis
slides
video 2021
Reading: Chapter 1, 2 of Kleinberg's book and Chapter 1 of Barabasi's book.
2
17.02.2021
Graphs and networks. Basic measures
slides
video 2021
Reading: Chapter 2 of Barabasi's book.
3
22.02.2021
Graphs and networks. Basic measures.slides
video 2021
Reading: Chapter 2 of Barabasi's book.
4
24.03.2021
Random networksslides
video
video 2021
Reading: Chapter 3 of Barabasi's book.
5
1.03.2021
It's a small world!slides
video
video 2021
Reading: Chapter 20 of Kleinberg’s book.
Milgram's 6 degrees paper.
WattsStrogatz's Small World paper.
6
3.03.2021
8.03.2021Scalefree networks slides
video1
video2
video 2021 (pt1)
video 2021 (pt2)Reading: Chapters 4 & 5 of Barabasi's book
BarabasiAlbert Preferential Attachment model.
7
10.03.2021
Centrality & Assortative Mixingslides
video
video 2021
Reading: Chapter 3 & 4 of Kleinberg's book
8
15.03.2021
Tie Strength & Resilienceslides
video
video 2021
Reading: Chapter 8 of Barabasi's book and Chapter 3 of Kleinberg's book9 17.03.2020 Gephi & Cytoscape Tutorial video
video 2021
10 22.03.2021 Community Discovery slides
video
video 2021
Reading: Chapter 9 of Barabasi's book
Survey: Community Detection in Graphs
Algorithm specific papers as reported in the slides11 24.03.2021 Dynamic Of Networks slides
video
Survey: Temporal Networks
Stream graphs and link streams for the modeling of interactions over time12 29.03.2021 Link Prediction
slides
videoSurvey: The link‐prediction problem for social networks. 13 31.03.2021 Dynamic Community Discovery slides
video
Challenges in community discovery on temporal networks
Survey: Community Discovery in Dynamic Networks: a Survey
147.04.2021
12.04.2021Diffusion: Decisionbased models
slides
video1
video2
video3
video4
video5
video6
Reading: Chapter 19 of Kleinberg's book
Threshold models of collective behavior
Book:
Rogers, E. M. “Diffusion of innovations”
15
14.04.2021
19.04.2021
Diffusion: Epidemics*
slides
video1
video2
video3Reading: Chapter 21 of Kleinberg's book and Chapter 10 of Barabasi's book
1621.04.2021 Diffusion: Opinion Dynamics
slides
video
Opinion dynamics: models, extensions and external effects.
Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model
1726.04.2021
Case Studies: Complex Network Analysis @KDD Lab
slides
video
1828.04.2021
TBD19 03.05.2021 TBD 20 05.05.2021 TBD 
Visual Tools:
Python >=3.7:
 Anaconda (Jupyter notebooks included)
 PyCharm IDE
 NetworkX
 Community Discovery: cdlib
 Diffusion and Epidemics: ndlib, ndlibrest
 Link Prediction: linkpred
Lectures' Notebooks:
Network Data Repository:

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
 CDlibviz: 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 (staticdynamic) networks in low dimensional space to support prediction and clustering
 High Order Networks: studying and modeling highorder temporal networks
 Countrywide AgentBased 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.