The Complex Networks section is dedicated to publishing research focused on the interdisciplinary study of network science and its applications.

Led by Dr. Claudio Castellano from Istituto dei Sistemi Complessi (ISC-CNR), the Complex Networks section welcomes submissions in the various domains of network science, which connect diverse scientific disciplines and emphasize the importance of topology in the phenomena under investigation.

Topics considered in the scope of this section include:

algorithmic complexity
coevolution of networks and dynamics
dynamics on networks (epidemic and information propagation, game theory, mobility and transport networks, online social media, ecological networks, opinion dynamics, etc.)
higher-order networks
micro and mesoscopic structures
multilayered networks
network growth and decay
network optimization
network spectra
network stability and robustness
signed networks
simulations of and on networks
small-world and scale-free networks
spatial networks
temporal networks

Submissions should provide detailed, in-depth knowledge about the role of topology in the functioning, efficiency, and stability of complex systems across various scientific disciplines.

In particular, the section welcomes submissions which support and advance the understanding of complex networks, algorithmic complexity, coevolution of networks and dynamics, dynamics on networks, micro and mesoscopic structures, multilayered networks, network growth and decay, network optimization, network spectra, network stability and robustness, signed networks, simulations of and on networks, small-world and scale-free networks, spatial networks, and temporal networks in relation to SDGs 3 (Good Health and Well-being), 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action).

This multidisciplinary section is at the forefront of disseminating and communicating cutting-edge scientific knowledge and impactful discoveries in the field of network science to researchers, industry, policymakers, and the public worldwide.

Classical and Quantum Approaches to Network Data Analysis

Insights in Complex Networks