Network Science of Socio-Economic Systems

Topics

The course will cover the following topics in Network Science:

• Network theory basics
• Network growth models
• Community detection algorithms
• Processes on Networks
• Network ensembles
• Financial and Economic Networks
• Social Networks

Format

The course takes the form of seminars and coding sessions:

1. 13 Seminars, the first 7 covering the main topics in Network Science, the following 6 covering more advanced techniques and applications of Network Science to Socio-Economic systems.
2. 1 Students Q&A Session
3. 7 Coding Sessions

Grading

Students select a published article from a set of readings or a dataset. The exam consist in reproducing the paper or analyzing the dataset using the techniques discussed in the course. The course grade is based on the student presentation (35%), the code (15%), and on the report (50%). Reports should be 4/5 pages long (including the bibliography) and must contain

1. Abstract
2. Introduction
3. Results
4. Discussion
5. Methods
6. References

Exam Dates

The dates of the exam are flexible. I will create a form to select some exam dates that suits you and publish the dates here.

Coding Sessions

A practical part of exercises connected to the lectures will be offered. It is highly recommended to participate in these coding sesisons because it will help to acquire the skills that are important for the final project. The exercises will not be graded.

Seminars

Coding Sessions

Seminars

Coding Sessions

• Friend of a friend models of network growth. Watson Levens, Alex Szorkovszky, and David J. T. Sumpter (2022)
• Explosive percolation in random networks. Dimitris Achlioptas, Raissa M D'Souza, Joel Spencer (2009)
• Emergence of Communities in Weighted Networks. Jussi M. Kumpula, Jukka-Pekka Onnela, Jari Saramäki, Kimmo Kaski, and János Kertész (2007)
• The role of endogenous and exogenous mechanisms in the formation of R&D networks. Mario V. Tomasello, Nicola Perra, Claudio J. Tessone, Márton Karsai & Frank Schweitzer (2014)
• Quantifying the complexity and similarity of chess openings using online chess community data. Giordano De Marzo and Vito DP Servedio (2023).
• Mapping job fitness and skill coherence into wages: an economic complexity analysis. Sabrina Aufiero, Giordano De Marzo, Angelica Sbardella, Andrea Zaccaria (2024)
• Error and attack tolerance of complex networks. Réka Albert, Hawoong Jeong and Albert-László Barabási (2000).
• Dynamically induced cascading failures in power grids. Benjamin Schäfer, Dirk Witthaut, Marc Timme & Vito Latora (2018)
• Growing scale-free networks with tunable clustering. Petter Holme and Beom Jun Kim (2002)
• Popularity versus similarity in growing networks. F. Papadopoulos et al. (2012)
• Ranking species in mutualistic networks. V. Domínguez-García and M. A. Muñoz (2015)
• The Scientific Competitiveness of Nations. G Cimini, A Gabrielli and F Sylos Labini (2014)
• Emergence of scale-free networks in social interactions among large language models. G. De Marzo, L. Pietronero and D. Garcia (2023)
• Error and attack tolerance of complex networks. P. Crucitti, V. Latora, M. Marchiori, A. Rapisarda (2004)
• The Anatomy of a Scientific Rumor. M. De Domenico, A. Lima, P. Mougel and M. Musolesi (2013)
• Economic development and wage inequality: A complex system analysis. A. Sbardella, E. Pugliese, L. Pietronero (2017)
• Fitness Centrality: a non-linear centrality measure for complex networks. V.D.P. Servedio et al. (2025)

• Wikipedia votes
• Facebook pages
• US power grid
• Co-authorship of scientists in network theory
• Hollywood collaboration network
• Facebook social network (Stanford students)
• Facebook social network (Michigan University students)
• Facebook social network (Northeastern University students)
• Dating network (bipartite)
• Github network (bipartite)
• Movies rating network (bipartite)