Complex networks articles within Nature Communications

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  • Article
    | Open Access

    Natural and synthetic networked systems can be characterized by synchrony or asynchrony of groups of their nodes (clusters) and undergo different scenarios of synchronization transitions. The authors propose an approach to predict the entire sequence of events that are taking place during the synchronization transition in networks of identical elements with any preferrable architecture.

    • Atiyeh Bayani
    • , Fahimeh Nazarimehr
    •  & Stefano Boccaletti

  • Article
    | Open Access

    Informal transportation services constitute the primary form of public transport in the Global South. Here, the authors analyze the structure of route networks in cities across the globe, showing how informal routes self-organize into consistent line services that often outperform centralized services in the Global North, exhibiting fewer detours and comparable interconnectivity.

    • Kush Mohan Mittal
    • , Marc Timme
    •  & Malte Schröder

  • Article
    | Open Access

    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

  • Article
    | Open Access

    Educational environment is known to influence learning efficiency of students, however qualitative analysis of this effect has open questions. The authors propose a model to quantify roommate peer effects based on student accommodation distribution and their academic performance.

    • Yi Cao
    • , Tao Zhou
    •  & Jian Gao

  • Article
    | Open Access

    Network memory impacts dynamical processes emerging in real-world social systems, however little is known about memory of temporal networks beyond pairwise interactions. The authors develop a framework to characterize the temporal organization of higher-order networks and propose a model of temporal hypergraphs with higher-order memory to reproduce the patterns emerging in real-world complex systems.

    • Luca Gallo
    • , Lucas Lacasa
    •  & Federico Battiston

  • Article
    | Open Access

    Reconstructing the structure of a complex networked system and predicting its time evolution to understand its functions are usually two subjects that are treated separately. The authors propose a theoretical framework based on information theory, that uncovers the relation between reconstructability and predictability in networked systems.

    • Charles Murphy
    • , Vincent Thibeault
    •  & Patrick Desrosiers

  • Article
    | Open Access

    Universality of critical behaviour of O(N) field theories on regular homogeneous lattices is established, but open questions remain for more complex lattices. Bighin et al. study universality on a non-homogeneous graph showing that its scaling theory is controlled by a single parameter, the spectral dimension.

    • Giacomo Bighin
    • , Tilman Enss
    •  & Nicolò Defenu

  • Article
    | Open Access

    Finding an optimal shape for transport networks, represented as multilayer structures, is a challenging problem. The authors propose analytical and computational frameworks to analyze sharp transitions from symmetric to asymmetric shapes in optimal networks, that can be applied for planning and development of improved multimodal transportation systems within a city.

    • Siddharth Patwardhan
    • , Marc Barthelemy
    •  & Filippo Radicchi

  • 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

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