The Mathematics of Large Networks Summer School focuses on discrete structures and their limits. It’ll take place in Budapest (Hungary) May 30-June 3, 2022.

The Mathematics of Large Networks Summer School is part of the Large networks and their limits (2022 Spring) semester. This summer school aims to bring together mathematicians and network scientists to foster the exchange of ideas between these two fields. During the school four minicourses will be given by distinguished researchers in graph theory and network science for students from both fields, who are interested in multidisciplinary approaches to networks. The main topics of the summer school include geometry of networks, dynamics on/of networks, higher order structures, network inference and applications.

Lecturers:

Ginestra Bianconi (Queen Mary University of London)
Remco van der Hofstad (TU Eindhoven)
Renaud Lambiotte (University of Oxford)
Kavita Ramanan (Brown University)

More at: erdoscenter.renyi.hu

16, 17, 18, 19, 22 (+23) November 2021, 11:00am – 06:00pm (GMT)

Online | SECOND CALL for applications >> until 14 October 2021

This CES Winter School is a 2nd Edition of the previously named “Sustainable development, complexity and change: thinking and practices for the SDG and other objectives” CES Winter School, held on December 2020. It is based on a logic of deep interdisciplinarity, oriented towards promoting productive, collaborative, critical and creative dialogues between different disciplines and modes of thinking, between theory and research and the practices that “in the real world” enact and realise, critique or present alternative or complementary proposals to current global challenges.

While the international political agenda is guided by the concept of sustainable development, both the concept and it’s expression, configured in the 17 SDG and their indicators, remain under discussion, raising issues about their adequacy to places, contexts and specific problems, about the practices that sustain the concept of sustainable development and the degree of congruence between the thinking underlying such political agendas, the complexity of the world and the actions informed by such thinking. The question needs to be raised that an insufficient recognition of the complexity of the problems that sustain local and global policies and the realities they aim to dress, as well as of the need to develop modes of thinking and practices congruent with such complexity, may prevent or limit the success of this international agenda, even leading, in unpredictable ways, to the configurations of new, more or less preferred or unwanted realities.

In this Winter School, we propose to address key global challenges, exploring a variety of critical, alternative and complementary views on how to address their complexity. As such, the School will combine lectures/seminars and guided and creative moments of group discussion aimed at the integration of knowledge and experiences towards the production of new ideas and projects.

More at: ces.uc.pt

Join our facilitated weekend hackathon Fri-Sun, November 12 – 14, 2021, taking place entirely online. Learn to apply Complexity Science toward solving important problems you’re passionate about within a team setting.

More at: www.complexityweekend.com

Tel Aviv University’s City Center is proud to invite you to the Crisis of Democracy in the Age of Cities,
​an international online conference.
The conference will last 3 consecutive days, from August 31st to September 2nd.
The Aim of the conference is to examine the links between the crisis of democracy with its tension between “non-democratic liberalism’ vs “non-liberal democracy’ and, the 21st century as the age of cities, in which the various properties of cities and urbanism dominate life. This, at the background of Industry 4.0, the Anthropocene, globalization and the COVID-19 pandemic.

Details at: en-urban.tau.ac.il

Networks provide a simple model to understand and predict the emergent behavior of complex systems made of a large number of interacting nonlinear dynamical units. Some of the most challenging and useful problems in network science focus on how structural properties of the underlying interaction network govern the collective dynamical behavior on the network, and how these rules can be discovered from real-world data. The rapid advancement of new machine learning techniques has led to the development of new algorithms and strategies for inference of underlying network structures in datasets, prediction of behavior of complex and dynamical networks, and identification and control of such networks.
In this second installment of the satellite, we are covering topics like machine-learning-based network inference problems, analysis of social networking and human behavior data, and prediction of behavior of complex systems. Social data analysis has emerged as an important topic in the recent years because we have seen a rapid growth of online polarization, and also because new data has been available for human social interactions. Using machine learning for dynamical systems has become important in fields like global climate prediction, and relevant for accelerating state-of-the-art simulations of complex systems.
This satellite meeting aims to bring together the network scientists having expertise in traditional approaches and expert machine learning scientists for exchange of ideas, and formation of a platform for future collaboration, as well as to deliberate upon open problems in the network science which can be addressed by machine learning techniques.

More at: sites.google.com