## Weekly outline

### Announcements

- 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.

- Groups can be composed of

### 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 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 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.

- Big graph data and social, information, biological and technological networks
### 2021 schedule and instructors

Schedule

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

Thursday, h 11:00 - 13:00, online#### Contacts

`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

#### 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

#### 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])- David Easley, Jon Kleinberg:
### Lecture calendar

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:

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.

Watts-Strogatz's Small World paper.

6

3.03.2021

8.03.2021Scale-free networks slides

video1

video2

video 2021 (pt1)

video 2021 (pt2)**Reading:**Chapters 4 & 5 of Barabasi's book

Barabasi-Albert 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: Decision-based models

slides

video1

video2

video3

video4

video5

video6

Reading:Threshold models of collective behavior

**Book:**

Rogers, E. M. “Diffusion of innovations”

15

14.04.2021

19.04.2021

Diffusion: Epidemics*

slides

video1

video2

video3**Reading:**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 ### Software, tutorials and datasets

**Visual Tools:****Python >=**3.7**:**- Anaconda (Jupyter notebooks included)
- PyCharm IDE
- NetworkX
- Community Discovery: cdlib
- Diffusion and Epidemics: ndlib, ndlib-rest
- Link Prediction: linkpred

**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.