Critical and collective effects in graphs and networks 2022 (CCEGN-2022)

May 16—20, 2022


Institutions helping organize the event:

  • Interdisciplinary Scientific Center J.V. Poncelet (CNRS IRL 2615),
  • Institute for Information Transmission Problems of the Russian Academy of Science,
  • Northeastern University (USA),
  • CNRS (France),
  • Moscow Independent University,
  • Euler Institute.

Organizing committee:

  • Dmitri Krioukov (Northeastern University, USA),
  • Alexander Gorsky (Institute for Information Transmission Problems, Russia),
  • Sergei Nechaev (Centre Poncelet, Moscow, Russia),
  • Alex Arenas (Universitat Rovira i Virgili, Tarragona, Spain),
  • Ginestra Bianconi (Queen Mary University, UK).

Advisory board:

  • Stanislav Smirnov (Steklov Institute, St. Petersburg),
  • Sidney Redner (Santafe University, USA),
  • Vincent Rivasseau (University Paris-Sud, Orsay, France),
  • Sergei Maslov (University of Illinois at Urbana-Champaign, USA),
  • Romualdo Pastor-Satorras (Universitat Politècnica de Catalunya, Spain).

Place: Euler Institute, St. Petersburg.


Network science is a hot and high-impact field. With critical behavior in topological random networks under scrutiny over the past decade, many questions dealing with phase transitions in graphs and networks spread far beyond pure academic interest and play a crucial role in various branches of physics (structure of membranes, formation of cosmic webs), technology (transport optimization, critical properties of electric networks), and life sciences and medicine (epidemic spread, structure of the connectome, analysis of genomic and protein networks). Investigating critical and collective effects in graphs and networks has picked up speed as a developing interdisciplinary area, with diverse applications and a variety of questions remaining to be answered. One of the most intriguing deals with the design of networks of special topology under evolution where some control parameters are changing. Many-body interactions, beyond the free-field theory, play a crucial role in network statistics. Those interactions lead to the emergence of phase transitions in complex distributed systems, and classical methods of the theory of complex networks are particularly useful in neurobiology and medicine, especially in the context of COVID-19. Throughout the workshop, we plan to focus on the statistical and dynamic properties of epidemic networks.


The specific subjects of the workshop are devoted to challenging modern research focuses. Despite the variety of applications, there are some common methods for studying critical phenomena in graphs and networks that are borrowed from spectral theory, statistical physics, and the physics of condensed matter. During the workshop, we plan to provide a balanced view of the applied research and fundamental methods available for testing the statistical and dynamic characteristics of systems. We plan in particular to focus our attention on the following questions:

  • Synchronization, spreading, and other dynamical processes in networks, including epidemic spread and COVID-19 propagation,
  • Spectral methods of network analysis, network entropy,
  • The metric structure of random graphs, hyperbolic embedding of random graphs,
  • Random graphs with quenched disorder, phase transitions, propagation of excitations on graphs, and related topics,
  • Statistical methods in interdisciplinary applications: social and ecological networks, genomic networks, neuroscience,
  • Dimensional reduction, compression, and restoration of big data by neural networks: principles and applications.

The workshop is interdisciplinary. Network science is currently becoming a unifying language for many branches of physics, computer science, mathematics, and biology, the same holding true for particular questions dealing with investigations into criticality and collective behavior in generic complex systems. The workshop provides a platform where these two avenues—networks and criticality—meet synergistically.

Thu Nov 18 2021 14:01:50 GMT+0300 (Moscow Standard Time)