Complex networks articles within Nature Communications

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  • Article
    | Open Access

    Neural network representations of quantum states are hoped to provide an efficient basis for numerical methods without the need for case-by-case trial wave functions. Here the authors show that limited generalization capacity of such representations is responsible for convergence problems for frustrated systems.

    • Tom Westerhout
    • , Nikita Astrakhantsev
    •  & Andrey A. Bagrov

  • Article
    | Open Access

    Honeybees have a sophisticated system to communicate foraging locations through a “dance”, but they also share food-related olfactory cues. Here, Hasenjager and colleagues use social network analysis to disentangle how foraging information is transmitted through these systems in different contexts.

    • Matthew J. Hasenjager
    • , William Hoppitt
    •  & Ellouise Leadbeater

  • Article
    | Open Access

    The design of future power grids with decentral control calls for a better understanding of the stability of synchronized networked systems. Here, Hellmann et al. show that the energy losses in coupled oscillators can significantly alter power grid dynamics by introducing solitary states in the network.

    • Frank Hellmann
    • , Paul Schultz
    •  & Yuri Maistrenko

  • Article
    | Open Access

    The likelihood of linking within a complex network is of importance to solve real-world problems, but it is challenging to predict. Sun et al. show that the link predictability limit can be well estimated by measuring the shortest compression length of a network without a need of prediction algorithm.

    • Jiachen Sun
    • , Ling Feng
    •  & Yanqing Hu

  • Article
    | Open Access

    Market integration may loosen the dense kinship networks maintaining high fertility among agriculturalists, but data are lacking. Here, Colleran shows that in 22 rural Polish communities, women’s ego networks are less kin-oriented, but not less dense, as market integration increases, potentially enabling low fertility values to spread.

    • Heidi Colleran

  • Article
    | Open Access

    Network properties can be modified when they interact with other networks, yet most previous results have focused on equilibrium states exclusively. Here the authors introduce a framework to examine the out-of-equilibrium dynamics of evolutionary processes to mimic real-world interconnected networks.

    • Javier M. Buldú
    • , Federico Pablo-Martí
    •  & Jacobo Aguirre

  • Article
    | Open Access

    The spatial structure of a population is often critical for the evolution of cooperation. Here, Allen and colleagues show that when spatial structure is represented by an isothermal graph, the effective number of neighbors per individual determines whether or not cooperation can evolve.

    • Benjamin Allen
    • , Gabor Lippner
    •  & Martin A. Nowak

  • Article
    | Open Access

    The 302-neuron connectome of the nematode C. elegans has been completely mapped, yet the design principles that explain how the connectome structure determines its function are unknown. Here, the authors show that physical principles of symmetry and mathematical tools of symmetry groups can be used to understand C. elegans neural locomotion circuits.

    • Flaviano Morone
    •  & Hernán A. Makse

  • Article
    | Open Access

    The growing availability of human mobility data can help assess the structure and dynamics of urban environments and their relation to the performance of cities. Here the authors introduce a metric of hierarchy in urban travel and find correlations between levels of hierarchy and other urban indicators.

    • Aleix Bassolas
    • , Hugo Barbosa-Filho
    •  & José J. Ramasco

  • Article
    | Open Access

    Linear controllability theories have stimulated research on control of complex networks. Here the authors investigate the concordance between linear and nonlinear approaches in ranking the importance of nodes in nonlinear networks, and conclude that linear controllability may not be applicable.

    • Junjie Jiang
    •  & Ying-Cheng Lai

  • Article
    | Open Access

    Systematic methods to characterize human mobility can lead to more accurate forecasting of epidemic spreading and better urban planning. Here the authors present a methodology to analyse daily commuting data by representing it with an irrotational vector field and a corresponding scalar potential.

    • Mattia Mazzoli
    • , Alex Molas
    •  & José J. Ramasco

  • Article
    | Open Access

    Non-equilibrium systems with hidden states are relevant for biological systems such as molecular motors. Here the authors introduce a method for quantifying irreversibility in such a system by exploiting the fluctuations in the waiting times of time series data.

    • Ignacio A. Martínez
    • , Gili Bisker
    •  & Juan M. R. Parrondo

  • Article
    | Open Access

    How does a scientist’s tendency to explore a variety of topics affect their career? Here, the authors analyze scientific publication data to understand how often scientists switch topics, how topic switching has changed over time, and how it relates to research productivity.

    • An Zeng
    • , Zhesi Shen
    •  & Shlomo Havlin

  • Article
    | Open Access

    Social contagion cannot only be understood in terms of pairwise interactions among individuals. Here, the authors include higher-order social interactions, the effects of groups, in their model of social contagion, enabling insight into why critical masses are required to initiate social changes.

    • Iacopo Iacopini
    • , Giovanni Petri
    •  & Vito Latora

  • Article
    | Open Access

    The spread of flood-induced failures in critical infrastructure systems is understudied. Here the authors apply the CaMa-Flood global river flood simulation model to estimate the flood-induced failures and their spread in China and the US and find that the number of flood-induced total failures is in-between that of random and localized damage given the same intensity.

    • Weiping Wang
    • , Saini Yang
    •  & Jianxi Gao

  • Article
    | Open Access

    Recovering the properties of a network which has suffered adversarial intervention can find applications in uncovering targeted attacks on social networks. Here the authors propose a causal statistical inference framework for reconstructing a network which has suffered non-random, targeted attacks.

    • Yuankun Xue
    •  & Paul Bogdan

  • Article
    | Open Access

    Supply demand equilibria in modern macroeconomic theories do not hold during recessionary shocks. Here the authors developed a non-equilibrium theory for the susceptibility of industrial sectors to shocks and showed these susceptibilities vary across countries, sectors and time and full economic recovery may take six to ten years.

    • Peter Klimek
    • , Sebastian Poledna
    •  & Stefan Thurner

  • Article
    | Open Access

    In areas with two or more spoken languages, linguistic shift may occur as speakers of one language switch to the other. Here, the authors show that linguistic shift is faster in rural compared to urban regions of Galicia, a bilingual community in Spain, due to the competition of internal complexity and network relevance.

    • Mariamo Mussa Juane
    • , Luis F. Seoane
    •  & Jorge Mira

  • Article
    | Open Access

    Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.

    • István A. Kovács
    • , Katja Luck
    •  & Albert-László Barabási

  • Comment
    | Open Access

    Are scale-free networks rare or universal? Important or not? We present the recent research about degree distributions of networks. This is a controversial topic, but, we argue, with some adjustments of the terminology, it does not have to be.

    • Petter Holme

  • Article
    | Open Access

    Real-world networks are often said to be ”scale free”, meaning their degree distribution follows a power law. Broido and Clauset perform statistical tests of this claim using a large and diverse corpus of real-world networks, showing that scale-free structure is far from universal.

    • Anna D. Broido
    •  & Aaron Clauset

  • Article
    | Open Access

    With ever-growing datasets, it is useful to filter out details and keep only the links that carry the relevant structural information. Here the authors generalize the disparity filter by providing a tolerance parameter that can be used to tune how strict the filter is in the selection of edges to preserve.

    • Riccardo Marcaccioli
    •  & Giacomo Livan

  • Article
    | Open Access

    Percolation is a tool used to investigate a network’s response as random links are removed. Here the author presents a generic analytic theory to describe how percolation properties are affected in coloured networks, where the colour can represent a network feature such as multiplexity or the belonging to a community.

    • Ivan Kryven

  • Article
    | Open Access

    Complex networks can be useful to describe social interactions but for large datasets one needs to identify significant links to extract information. While most existing methods work for static networks, here the authors propose a method to extract the backbone of significant links in temporal networks.

    • Teruyoshi Kobayashi
    • , Taro Takaguchi
    •  & Alain Barrat

  • Article
    | Open Access

    The influence of 'fake news’, spread via social media, has been much discussed in the context of the 2016 US presidential election. Here, the authors use data on 30 million tweets to show how content classified as fake news diffused on Twitter before the election.

    • Alexandre Bovet
    •  & Hernán A. Makse

  • Article
    | Open Access

    Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

    • Chengcheng Shao
    • , Giovanni Luca Ciampaglia
    •  & Filippo Menczer

  • Article
    | Open Access

    Digital traces of our lives have the potential to allow insights into collective behaviors. Here, the authors cluster consumers by their credit card purchase sequences and discover five distinct groups, within which individuals also share similar mobility and demographic attributes.

    • Riccardo Di Clemente
    • , Miguel Luengo-Oroz
    •  & Marta C. González

  • Article
    | Open Access

    Community detection allows one to decompose a network into its building blocks. While communities can be identified with a variety of methods, their relative importance can’t be easily derived. Here the authors introduce an algorithm to identify modules which are most promising for further analysis.

    • Marinka Zitnik
    • , Rok Sosič
    •  & Jure Leskovec

  • Article
    | Open Access

    Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

    • Decebal Constantin Mocanu
    • , Elena Mocanu
    •  & Antonio Liotta

  • Article
    | Open Access

    Complex networks can be used to model brain networks. Here the authors identify the essential nodes in a model of a brain network and then validate these predictions by means of in vivo pharmacogenetic interventions. They find that the nucleus accumbens is a central region for brain integration.

    • Gino Del Ferraro
    • , Andrea Moreno
    •  & Hernán A. Makse

  • Article
    | Open Access

    How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

    • Ana P. Millán
    • , J. J. Torres
    •  & J Marro

  • Article
    | Open Access

    Communication networks and power grids may be subject to cascading failures which can lead to outages. Here the authors propose to investigate cascades using dynamical transients of electrical power grids, thereby identifying possible vulnerabilities that might remain undetected with any static approach.

    • Benjamin Schäfer
    • , Dirk Witthaut
    •  & Vito Latora

  • Article
    | Open Access

    Understanding global epidemics spread is crucial for preparedness and response. Here the authors introduce an analytical framework to study epidemic spread on air transport networks, and demonstrate its power to estimate key epidemic parameters by application to the recent influenza pandemic and Ebola outbreak.

    • Lin Wang
    •  & Joseph T. Wu

  • Article
    | Open Access

    Most time series techniques tend to ignore data uncertainties, which results in inaccurate conclusions. Here, Goswami et al. represent time series as a sequence of probability density functions, and reliably detect abrupt transitions by identifying communities in probabilistic recurrence networks.

    • Bedartha Goswami
    • , Niklas Boers
    •  & Jürgen Kurths

  • Article
    | Open Access

    Complex networks are a useful tool to investigate the structure of cities and their street networks. Here the authors investigate the shape of travel routes in 92 cities and define a metric called inness which reveals connections between common urban features in cities with similar inness profiles.

    • Minjin Lee
    • , Hugo Barbosa
    •  & Gourab Ghoshal

  • Article
    | Open Access

    Network dynamical systems can represent the interactions involved in the collective dynamics of gene regulatory networks or metabolic circuits. Here Casadiego et al. present a method for inferring these types of interactions directly from observed time series without relying on their model.

    • Jose Casadiego
    • , Mor Nitzan
    •  & Marc Timme

  • Article
    | Open Access

    Complex networks are a useful tool to investigate spreading processes but topology alone is insufficient to predict information flow. Here the authors propose a measure of information flow and predict its behavior from the interplay between structure and dynamics.

    • Uzi Harush
    •  & Baruch Barzel

  • Article
    | Open Access

    Mass drug administration depends on the distributors’ contact with community members. Using data of deworming treatment distribution from Ugandan villages, the authors show that community medicine distributors with tightly-knit friendship connections achieve the greatest reach and speed of coverage.

    • Goylette F. Chami
    • , Andreas A. Kontoleon
    •  & David W. Dunne

  • Article
    | Open Access

    Heterogeneous complex networks tend to be a more realistic representation of social networks than homogenous ones. Here Kleineberg investigates the role of network heterogeneity in the emergence of cooperation in social dilemmas and shows that it can sometimes hinder it.

    • Kaj-Kolja Kleineberg

  • Article
    | Open Access

    Cities can be treated as dynamic complex systems being controlled by the interactions among people, whilst the detail remains largely unknown. Li et al. use spatial attraction together with matching growth to unify population, roads, and socioeconomic interactions crossing ten representative cities.

    • Ruiqi Li
    • , Lei Dong
    •  & H. Eugene Stanley

  • Article
    | Open Access

    Understanding and accurate prediction of human mobility is of increasing importance, but a universal framework is lacking. Here, the authors develop a unified model that accurately predicts both individual and population mobility and scaling behaviors on diverse spatial scales.

    • Xiao-Yong Yan
    • , Wen-Xu Wang
    •  & Ying-Cheng Lai

  • Article
    | Open Access

    Mapping complex networks to underlying geometric spaces can help understand the structure of networked systems. Here the authors propose a class of machine learning algorithms for efficient embedding of large real networks to the hyperbolic space, with potential impact on big network data analysis.

    • Alessandro Muscoloni
    • , Josephine Maria Thomas
    •  & Carlo Vittorio Cannistraci

  • Article
    | Open Access

    Multiplex networks consist of a collection of interacting layers and occur in social and technological systems. Here Osat et al. investigate optimal percolation which relates to the process of optimally dismantling multiplex networks and show that simplified versions of this problem lead to error.

    • Saeed Osat
    • , Ali Faqeeh
    •  & Filippo Radicchi

  • Article
    | Open Access

    Complex networks represent systems such as neural networks and air traffic as interconnected nodes that organize themselves into subsets. Here Bertolero et al. propose a subset which they call the diverse club, which offers an alternative to the commonly used rich club.

    • M. A. Bertolero
    • , B. T. T. Yeo
    •  & M. D’Esposito

  • Article
    | Open Access

    Cancer is caused by accumulating genetic mutations. Here, the authors investigate the cooperative effect of these mutations in colorectal cancer patients and identify a giant cluster of mutation-propagating modules that undergoes percolation transition during tumorigenesis.

    • Dongkwan Shin
    • , Jonghoon Lee
    •  & Kwang-Hyun Cho

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