Month: February 2026

Complex Networks Theory, Methods, and Applications

10th edition
May 18-22, 2026
Villa del Grumello,
Como, Italy
Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline – including sociology, transportation, economics and finance, biology, and myriad others – and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

Read the full article at: ntml.lakecomoschool.org

Complexity72h 2026 – Call for Participants

Complexity72h is a cross-disciplinary workshop where young researchers work in small interdisciplinary teams on a real research project in complex systems over 72 intense hours.

📍 June 21–26, 2026 | Northeastern University London
👩‍🔬 Open to Master’s students, PhD students, and postdocs
📌 Application deadline: February 28th, 2026

Registration fee: €710 (includes 5 nights accommodation, workshop facilities, coffee breaks, lunches, invited lectures, and social events).

More information and applications: https://complexity72h.com 

Discovering network dynamics with neural symbolic regression

Zihan Yu, Jingtao Ding & Yong Li 
Nature Computational Science (2025)

Network dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems, it corrects existing models of gene regulation and microbial communities, reducing prediction error by 59.98% and 55.94%, respectively. In epidemic transmission across human mobility networks of various scales, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.

Read the full article at: www.nature.com