Complex Networks in Physics: Data-Driven Construction, Dynamics, and Applications

Complex network theory has become a key analytical framework in modern physics for studying structure, dynamics, and emergent behaviour in complex systems. Beyond abstract graph models, network-based approaches are now widely used to analyse physical phenomena using empirical data, numerical simulations, and hybrid data–theory pipelines.A major recent development is the data-driven construction of networks, where time series, spatial fields, or multivariate physical observations are transformed into network representations. Such mappings enable physicists to investigate nonlinear dynamics, predictability, stability, and regime transitions using graph-theoretic concepts. These methods complement classical modelling by offering interpretable, scalable tools that can handle noisy and high-dimensional data.This Research Topic aims to bring together theoretical, methodological, and applied contributions on complex networks in physics, with a focus on how networks are constructed from physical data, how dynamics unfold on and of networks, and how network representations support modeling, inference, and forecasting.The scope is intentionally broad, covering both foundational developments and application-driven studies, while maintaining a clear unifying theme: complex networks as a physics-informed tool for understanding and predicting physical systems.Scope and TopicsManuscripts may address (but are not limited to):1. Network Theory for Physical Systems• Structural and spectral properties of physical networks• Stability, robustness, and phase transitions• Temporal, multilayer, and adaptive networks2. Data-Driven Network Construction• Time series–induced networks (e.g., visibility graphs, recurrence networks, symbolic and transition networks)• Networks from spatial and spatiotemporal data• Statistical and information-theoretic network inference• Uncertainty, noise, and finite-data effects3. Dynamics on and of Networks• Transport, diffusion, synchronisation, and spreading processes• Co-evolution of dynamics and topology• Control and intervention in networked systems4. Network-Based Modelling and Forecasting• Prediction of nonlinear and chaotic systems• Early-warning signals and regime-shift detection• Physics-informed and hybrid network–machine learning models5. Applications in Physics• Climate and geophysical systems• Energy systems and power grids• Fluid dynamics and turbulence• Biological and soft-matter physics• Materials and transport networksArticle Types• Original Research• Review• Methods• Brief Research Report• PerspectiveExpected ImpactThis collection will consolidate emerging work on network-centric modelling in physics, promote reproducible data-driven methodologies, and encourage cross-disciplinary exchange between nonlinear dynamics, statistical physics, and network science. It is expected to serve as a reference for researchers using networks to analyse and predict complex physical systems. Topic editors: Satheesh Kumar, BALASUBRAMANIAM S, Asokan K, Rajan Jose Source: https://www.frontiersin.org/research-topics/79184/complex-networks-in-physics-data-driven-construction-dynamics-and-applications