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Showing 1–11 of 11 results
Advanced filters: Author: Márton Pósfai Clear advanced filters

  • The control of a complex network can be achieved by different combinations of relatively few driver nodes. Tao Jia and colleagues show that this can lead to two distinct control modes—centralized or distributed—that determine the number of nodes that can act as driver node.

    • Tao Jia
    • Yang-Yu Liu
    • Albert-László Barabási
    Research
    Nature Communications
    Volume: 4, P: 1-6

  • Physical networks are systems composed of physical entities, which conventional graph-based approaches fail to capture. Theoretical work now introduces a meta-graph technique to uncover the impact of physicality on the structure of networks.

    • Márton Pósfai
    • Balázs Szegedy
    • Albert-László Barabási
    Research
    Nature Physics
    Volume: 20, P: 142-149

  • In analysis of physically embedded complex networks, their nodes are usually considered as localized spheres connected by links, neglecting possible differences of nodes spatial shapes. The authors develop a representation of physical networks that captures arbitrary node shapes to characterize structural and dynamical network properties.

    • Gábor Pete
    • Ádám Timár
    • Márton Pósfai
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-8

  • From airline routes to blood vessels and neural connectomes, the physical layout of a network shapes how it functions and survives damage. Our study shows that spatial disruptions rapidly dismantle networks with elongated links, highlighting the impact of morphology on network robustness.

    • Luka Blagojević
    • Ivan Bonamassa
    • Márton Pósfai
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-13

  • What is the path towards a physical theory of complex networked systems? With an eye to the historical maths-physics duality, and an outlook towards the future, this commentary discusses promises and challenges accompanying the convergence of formal graph theory and data-inspired network science.

    • Gerardo Iñiguez
    • Federico Battiston
    • Márton Karsai
    Comments & OpinionOpen Access
    Communications Physics
    Volume: 3, P: 1-5