Complex networks articles within Nature Communications

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  • Article
    | Open Access

    Thurner and colleagues explore how economic shocks spread risk through the globalized economy. They find that rich countries expose poor countries stronger to systemic risk than vice-versa. The risk is highly concentrated, however higher risk levels are not compensated with a risk premium in GDP levels, nor higher GDP growth. The findings put the often-praised benefits for developing countries from globalized production in a new light, by relating them to risks involved in the production processes

    • Abhijit Chakraborty
    • , Tobias Reisch
    •  & Stefan Thurner

  • Article
    | Open Access

    Collective cooperation is found across many social and biological systems. Here, the authors find that infrequent hub updates promote the emergence of collective cooperation and develop an algorithm that optimises collective cooperation with update rates.

    • Yao Meng
    • , Sean P. Cornelius
    •  & Aming Li

  • Article
    | Open Access

    Evolution processes of complex networked systems in biology and social sciences, and their underlying mechanisms, still need better understanding. The authors propose a machine learning approach to reconstruct the evolution history of complex networks.

    • Junya Wang
    • , Yi-Jiao Zhang
    •  & Yanqing Hu

  • Article
    | Open Access

    For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.

    • Xin Li
    • , Qunxi Zhu
    •  & Wei Lin

  • Article
    | Open Access

    Early warning signals for rapid regime shifts in complex networks are of importance for ecology, climate and epidemics, where heterogeneities in network nodes and connectivity make construction of early warning signals challenging. The authors propose a method for selecting an optimal set of nodes from which a reliable early warning signal can be obtained.

    • Naoki Masuda
    • , Kazuyuki Aihara
    •  & Neil G. MacLaren

  • Article
    | Open Access

    Identification of nodes that play a crucial role in the complex network functionality is of high relevance for supply, transportation, and epidemic spreading networks. The authors propose a metric to evaluate nodal dominance based on competition dynamics that integrate local and global topological information, revealing fragile structures in complex networks.

    • Marcus Engsig
    • , Alejandro Tejedor
    •  & Chaouki Kasmi

  • Article
    | Open Access

    Approaches for assessing epidemic risks meet challenges when dealing with high-resolution data available nowadays, that includes behaviors, disease progression, and interventions. The authors propose an analytical framework to compute the epidemic threshold for arbitrary models of diseases, interventions, and hosts contact patterns.

    • Eugenio Valdano
    • , Davide Colombi
    •  & Vittoria Colizza

  • Article
    | Open Access

    Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. The authors propose a framework to unveil identifiable early signals and predict the eventual outcome of traffic bottlenecks, which may be useful for designing effective methods preventing traffic jams.

    • Jinxiao Duan
    • , Guanwen Zeng
    •  & Shlomo Havlin

  • Article
    | Open Access

    Embedding of complex networks in the latent geometry allows for a better understanding of their features. The authors propose a framework for mapping complex networks into high-dimensional hyperbolic space to capture their intrinsic dimensionality, navigability and community structure.

    • Robert Jankowski
    • , Antoine Allard
    •  & M. Ángeles Serrano

  • Article
    | Open Access

    Degree distributions are often used as informative descriptions of complex networks, however previous studies mainly focused on characterizing the tail of the distribution. The authors propose an evolutionary model that integrates the weight and degree of a node, which allows to better capture degree and degree ratio distributions of real networks and replicate their evolution processes.

    • Bin Zhou
    • , Petter Holme
    •  & Xiangyi Meng

  • Article
    | Open Access

    The authors use a complexity-based approach to analyze Arctic weather variability. They identify a pronounced link between the Arctic’s shrinking sea ice and global weather patterns, underscoring the critical role of the Arctic in shaping global climate.

    • Jun Meng
    • , Jingfang Fan
    •  & Jürgen Kurths

  • Article
    | Open Access

    Networks with higher-order interactions provide better description of social and biological systems, however tools to analyze their function still need to be developed. The authors introduce here a decomposition of network in hyper-cores, that gives better understanding of spreading processes and can be applied to fingerprint real-world datasets.

    • Marco Mancastroppa
    • , Iacopo Iacopini
    •  & Alain Barrat

  • Article
    | Open Access

    Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.

    • Gerardo Iñiguez
    • , Sara Heydari
    •  & Jari Saramäki

  • Article
    | Open Access

    Achieving shape assembly behaviour in robot swarms with adaptability and efficiency is challenging. Here, Sun et. al. propose a strategy based on an adapted mean-shift algorithm, thus realizing complex shape assembly tasks such as shape regeneration, cargo transportation, and environment exploration.

    • Guibin Sun
    • , Rui Zhou
    •  & Shiyu Zhao

  • Article
    | Open Access

    Understanding of diffusive and spreading processes in networks remains challenging when dynamics of the network is complex. The authors propose a quantity to reflect the potential of a network node to diffuse information, that may serve to develop interventions for improved network efficiency.

    • Tiago A. Schieber
    • , Laura C. Carpi
    •  & Martín G. Ravetti

  • Article
    | Open Access

    Supercritical fluids have local density inhomogeneities caused by molecular clusters. Authors show that the molecular interactions of supercritical fluids, associated with localized clusters, obey complex network dynamics that can be represented by a hidden-variable network model.

    • Filip Simeski
    •  & Matthias Ihme

  • Article
    | Open Access

    State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions.

    • Ayan Chatterjee
    • , Robin Walters
    •  & Giulia Menichetti

  • Article
    | Open Access

    Authors model programmable photonic circuits targeting universal unitaries and verify that a type of unit rotation operator has a heavy-tailed distribution. They suggest hardware pruning for random unitary and present design strategies for high fidelity and energy efficiency in large-scale quantum computations and photonic deep learning accelerators.

    • Sunkyu Yu
    •  & Namkyoo Park

  • Article
    | Open Access

    Visualization of large complex networks is challenging with limitations for the network size and depicting specific structures. The authors propose a Graph Neural Network based algorithm with improved speed and the quality of graph layouts, which allows for fast and informative visualization of large networks.

    • Csaba Both
    • , Nima Dehmamy
    •  & Albert-László Barabási

  • Article
    | Open Access

    Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals.

    • Mattia Mazzoli
    • , Riccardo Gallotti
    •  & José J. Ramasco

  • Article
    | Open Access

    Triadic interactions are higher-order interactions relevant to many real complex systems. The authors develop a percolation theory for networks with triadic interactions and identify basic mechanisms for observing dynamical changes of the giant component such as the ones occurring in neuronal and climate networks.

    • Hanlin Sun
    • , Filippo Radicchi
    •  & Ginestra Bianconi

  • Article
    | Open Access

    Shortest paths between the nodes of complex networks are challenging to obtain if the information on network structure is incomplete. Here the authors show that the shortest paths are geometrically localized in hyperbolic representations of networks, and can be detected even if the large amount of network links are missing. The authors demonstrate the utility of geometric pathfinding in Internet routing and the reconstruction of cellular pathways.

    • Maksim Kitsak
    • , Alexander Ganin
    •  & Igor Linkov

  • Article
    | Open Access

    Here the authors introduce dual communities, characterized by strong connections at their boundaries, and show that they are formed as a trade-off between efficiency and resilience in supply networks.

    • Franz Kaiser
    • , Philipp C. Böttcher
    •  & Dirk Witthaut

  • Article
    | Open Access

    The manifold’s geometry underlying the connectivity of a complex network determines its navigation ruled by the nodes distances in the geometrical space. In this work, the authors propose an algorithm which allows to uncover the relation between the measures of geometrical congruency and efficient greedy navigability in complex networks.

    • Carlo Vittorio Cannistraci
    •  & Alessandro Muscoloni

  • Article
    | Open Access

    Networks with higher-order interactions are known to provide better representation of real networked systems. Here the authors introduce a framework based on statistical inference to detect overlapping communities and predict hyperedges of any size in hypergraphs.

    • Martina Contisciani
    • , Federico Battiston
    •  & Caterina De Bacco

  • Article
    | Open Access

    Adding prior experimentally or theoretically obtained knowledge to the training of recurrent neural networks may be challenging due to their feedback nature with arbitrarily long memories. The authors propose a path sampling approach that allows to include generic thermodynamic or kinetic constraints for learning of time series relevant to molecular dynamics and quantum systems.

    • Sun-Ting Tsai
    • , Eric Fields
    •  & Pratyush Tiwary

  • Perspective
    | Open Access

    Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.

    • Leto Peel
    • , Tiago P. Peixoto
    •  & Manlio De Domenico

  • Article
    | Open Access

    Reducing of dimension is often necessary to detect and analyze patterns in large datasets and complex networks. Here, the authors propose a method for detection of the intrinsic dimensionality of high-dimensional networks to reproduce their complex structure using a reduced tractable geometric representation.

    • Pedro Almagro
    • , Marián Boguñá
    •  & M. Ángeles Serrano

  • Article
    | Open Access

    A new study finds that city growth in the U.S. is spatially heterogeneous. Inter-city flows concentrate in core areas. Intra-city flows are generally directed towards external and low density counties of cities, and is the main contributor to urban sprawl.

    • Sandro M. Reia
    • , P. Suresh C. Rao
    •  & Satish V. Ukkusuri

  • Article
    | Open Access

    Socioeconomic segregation is one of the main factors behind large-scale inequalities in urban areas and its characterisation remains challenging. The authors propose a family of non-parametric measures to quantify spatial heterogeneity through diffusion, and show how this relates to segregation and deprivation

    • Sandro Sousa
    •  & Vincenzo Nicosia

  • Article
    | Open Access

    Increasing the capacity of existing lines or adding new lines in power grids may, counterintuitively, reduce the system performance and promote blackouts. The authors propose an approach for prediction of edges that lower system performance and defining potential constrains for grid extensions.

    • Benjamin Schäfer
    • , Thiemo Pesch
    •  & Marc Timme

  • Article
    | Open Access

    Spreading processes and cascading failures on complex networks are often triggered by external perturbations. The authors uncover the impact of network motifs on the processes of perturbations propagation through networks, and networks’ response dynamics.

    • Xiaoge Bao
    • , Qitong Hu
    •  & Jan Nagler

  • Article
    | Open Access

    Dissipatively coupled oscillators, describing lossy flows in power grids, are challenging to analyze due to asymmetry of couplings. Here, Delabays et al. reveal counterintuitive behaviours of increased capacity and increased stability in a network of lossy oscillators.

    • Robin Delabays
    • , Saber Jafarpour
    •  & Francesco Bullo

  • Article
    | Open Access

    Networks with higher-order interactions are relevant to variety of real-world applications, they can be good description of data even if the system has only pairwise interactions. The authors uncover the hypernetwork emergence in coupled nonlinear oscillators and electrochemical experiments.

    • Eddie Nijholt
    • , Jorge Luis Ocampo-Espindola
    •  & Tiago Pereira

  • Article
    | Open Access

    Transportation networks undergo permanent changes influenced by a variety of human-induced and natural factors. The authors propose here a machine learning framework for prediction of connections removal that could be useful in building scenarios for transportation infrastructure needs.

    • Weihua Lei
    • , Luiz G. A. Alves
    •  & Luís A. Nunes Amaral

  • Article
    | Open Access

    Boolean networks modelling various biological processes are characterized by nonlinear reversible dynamics that makes their control challenging. The authors introduce extended concepts of influence and control, typically considered in the study of spreading processes, for Boolean dynamics.

    • Thomas Parmer
    • , Luis M. Rocha
    •  & Filippo Radicchi

  • Article
    | Open Access

    Defining the dimension in bounded, inhomogeneous or discrete physical systems may be challenging. The authors introduce here a dynamics-based notion of dimension by analysing diffusive processes in space, relevant for non-ideal physical systems and networks.

    • Robert Peach
    • , Alexis Arnaudon
    •  & Mauricio Barahona

  • Article
    | Open Access

    Rumors and information spreading emerge naturally from human-to-human interaction and have a growing impact on people’s lives due to increasing and faster access to information, whether trustworthy or not. The authors study the Maki–Thompson rumor model and its variation, revealing a phase transition and providing insights into the nature of this transition.

    • Guilherme Ferraz de Arruda
    • , Lucas G. S. Jeub
    •  & Yamir Moreno

  • Article
    | Open Access

    Data-driven recovery of topology is challenging for networks beyond pairwise interactions. The authors propose a framework to reconstruct complex networks with higher-order interactions from time series, focusing on networks with 2-simplexes where social contagion and Ising dynamics generate binary data.

    • Huan Wang
    • , Chuang Ma
    •  & Hai-Feng Zhang

  • Article
    | Open Access

    Pre-exposure prophylaxis (PrEP) is an effective HIV prevention measure but identifying those most at risk to target for treatment is challenging. Here, the authors demonstrate that non-selective PrEP distribution outperforms targeted strategies when use is not consistent, and/or prevalence of untreated HIV is high.

    • Benjamin Steinegger
    • , Iacopo Iacopini
    •  & Eugenio Valdano

  • Article
    | Open Access

    Modern power grids undergo a transition due to the integration of renewable energy generation technologies that bring heterogeneity in the grid. The authors study the synchronization and stability of power grids with heterogeneous inertia and damping factors, and demonstrate power feasibility of operating a system consisting of only renewable generation technologies with enhanced stability.

    • Amirhossein Sajadi
    • , Rick Wallace Kenyon
    •  & Bri-Mathias Hodge

  • Article
    | Open Access

    Information about an individual’s mobility can leave traces embedded in the social network. The authors show that such traces are also present beyond the social network. Simple colocation contains predictive information about one’s mobility patterns even when the colocators have no social links. In the aggregate, non-social information can sometimes meet or exceed social information.

    • Zexun Chen
    • , Sean Kelty
    •  & Gourab Ghoshal

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