Search results for "Complex networks"

showing 10 items of 49 documents

Stochastic sampling effects favor manual over digital contact tracing.

2020

Isolation of symptomatic individuals, tracing and testing of their nonsymptomatic contacts are fundamental strategies for mitigating the current COVID-19 pandemic. The breaking of contagion chains relies on two complementary strategies: manual reconstruction of contacts based on interviews and a digital (app-based) privacy-preserving contact tracing. We compare their effectiveness using model parameters tailored to describe SARS-CoV-2 diffusion within the activity-driven model, a general empirically validated framework for network dynamics. We show that, even for equal probability of tracing a contact, manual tracing robustly performs better than the digital protocol, also taking into accou…

0301 basic medicinePhysics - Physics and SocietyComputer scienceEpidemiologyScienceComplex networksFOS: Physical sciencesGeneral Physics and AstronomyPhysics and Society (physics.soc-ph)Tracingcomputer.software_genreGeneral Biochemistry Genetics and Molecular BiologyArticleSpecimen Handling03 medical and health sciences0302 clinical medicineHumans030212 general & internal medicineQuantitative Biology - Populations and EvolutionPandemicsCondensed Matter - Statistical Mechanicsstochastic modelProtocol (science)Stochastic ProcessesMultidisciplinaryStatistical Mechanics (cond-mat.stat-mech)Stochastic processDiagnostic Tests RoutineSARS-CoV-2QPopulations and Evolution (q-bio.PE)Sampling (statistics)COVID-19General ChemistryComplex networkModels TheoreticalNetwork dynamics030104 developmental biologyFOS: Biological sciencesScalabilityQuarantineData miningContact TracingcomputerContact tracingAlgorithmsNature communications
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Statistical characterization of deviations from planned flight trajectories in air traffic management

2016

Understanding the relation between planned and realized flight trajectories and the determinants of flight deviations is of great importance in air traffic management. In this paper we perform an in depth investigation of the statistical properties of planned and realized air traffic on the German airspace during a 28 day periods, corresponding to an AIRAC cycle. We find that realized trajectories are on average shorter than planned ones and this effect is stronger during night-time than daytime. Flights are more frequently deviated close to the departure airport and at a relatively large angle to destination. Moreover, the probability of a deviation is higher in low traffic phases. All the…

0301 basic medicinePhysics - Physics and SocietyEngineeringStrategy and ManagementFOS: Physical sciencesTransportationPhysics and Society (physics.soc-ph)Management Monitoring Policy and LawStability (probability)socio-technical complex systemAviation safety03 medical and health sciencesControl theory0502 economics and business11. SustainabilityAir traffic management complex networks trajectoriesSimulationRandomness050210 logistics & transportationbusiness.industry05 social sciencesAir traffic managementAir traffic controlSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)030104 developmental biologyMetric (mathematics)TrajectoryNull hypothesisbusinessLawair traffc managementJournal of Air Transport Management
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Complex Networks in Air Transport

2016

The application of CNT to air traffic management has seen significant growth in recent years. This is partly because air traffic can be seen as the superposition of different networks, including the networks of airports, sectors and navigation points. Moreover each of these networks can be seen as a multiplex – for example, by associating each layer with a different airline. The study of the topology of these networks is important for several reasons related to understanding, monitoring, controlling, and optimising the air traffic system. The topological properties of air traffic networks are useful: (i) for studying how the air traffic has changed in recent years; (ii) for identifying the …

Air transportGeographybusiness.industrySystems engineeringcomplex networks air traffic management transportationArtificial intelligenceComplex networkbusinessSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)
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An approach for influence estimatation in complex networks : application to the social network Twitter

2019

Influence in complex networks and in particular Twitter has become recently a hot research topic. Detecting most influential users leads to reach a large-scale information diffusion area at low cost, something very useful in marketing or political campaigns. In this thesis, we propose a new approach that considers the several relations between users in order to assess influence in complex networks such as Twitter. We model Twitter as a multiplex heterogeneous network where users, tweets and objects are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies).The multiplex PageRank is applied to data from two datasets in the political…

Belief functions teory[INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY][INFO.INFO-WB] Computer Science [cs]/WebRéseaux multiplexesThéorie des fonctions de croyanceTwitterComplex networksRéseaux sociauxSocial networks
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Choosing Optimal Seed Nodes in Competitive Contagion.

2019

International audience; In recent years there has been a growing interest in simulating competitive markets to find out the efficient ways to advertise a product or spread an ideology. Along this line, we consider a binary competitive contagion process where two infections, A and B, interact with each other and diffuse simultaneously in a network. We investigate which is the best centrality measure to find out the seed nodes a company should adopt in the presence of rivals so that it can maximize its influence. These nodes can be used as the initial spreaders or advertisers by firms when two firms compete with each other. Each node is assigned a price tag to become an initial advertiser whi…

Big Datagame theoryComputer scienceProcess (engineering)01 natural sciencescompetitive contagionMicroeconomics010104 statistics & probabilityArtificial IntelligenceNode (computer science)Computer Science (miscellaneous)seed nodes0101 mathematicsOriginal ResearchSmall numbercentrality measures010102 general mathematicsStochastic game[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]complex networksComplex networkProduct (business)CentralityGame theorycompetitive marketingInformation SystemsFrontiers in big data
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A Review of Mathematical and Computational Methods in Cancer Dynamics.

2022

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the si…

Cancer Researchinverse problemssystems oncologyFOS: Physical sciencescomplex networksdynamical systemsOther Quantitative Biology (q-bio.OT)Nonlinear Sciences - Chaotic DynamicsalgorithmsQuantitative Biology - Other Quantitative BiologyOncologyFOS: Biological sciencescancerChaotic Dynamics (nlin.CD)complexity scienceinformation theory
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World Influence of Infectious Diseases from Wikipedia Network Analysis

2019

AbstractWe consider the network of 5 416 537 articles of English Wikipedia extracted in 2017. Using the recent reduced Google matrix (REGOMAX) method we construct the reduced network of 230 articles (nodes) of infectious diseases and 195 articles of world countries. This method generates the reduced directed network between all 425 nodes taking into account all direct and indirect links with pathways via the huge global network. PageRank and CheiRank algorithms are used to determine the most influential diseases with the top PageRank diseases being Tuberculosis, HIV/AIDS and Malaria. From the reduced Google matrix we determine the sensitivity of world countries to specific diseases integrat…

CheiRankComputer scienceHuman immunodeficiency virus (HIV)medicine.disease_cause01 natural sciences[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]law.invention03 medical and health sciencesPageRanklaw0103 physical sciencesGlobal networkmedicine010306 general physics030304 developmental biology0303 health sciencesInformation retrievalGoogle matrixMarkov processes[PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph]complex networksdata mining[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]ranking (statistics)3. Good healthInfectious diseaseslcsh:Electrical engineering. Electronics. Nuclear engineeringlcsh:TK1-9971Network analysisWikipedia
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Preface

2019

The Enrico Fermi Schools are a highly prestigious series of summer schools of the Italian Physical Society with a tradition of more than 60 years and with many Nobel laureates as lecturers (https://www.sif.it/attivita/scuola_fermi/). The International Schools devote special care in planning the program and produces proceedings of the school that have become classics. Recently an increasing number of interdisciplinary topics have been selected and our school fits into this trend. Our school will consider complex systems of social and economic origin by teaching and discussing concepts and topics of computational social science and econophysics. These are fields, where physicists, computer sc…

Complex Systems Complex networks Econophysics Computational social science Epidemic spreadingSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)
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Complex networks : application for texture characterization and classification

2008

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

Computer engineering. Computer hardwareTexture compressionComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComplex networksImage processingTexture (geology)TK7885-7895Image textureImage processingAnàlisi de texturaProcesamiento de imágenestexture analysisClustering coefficientAnálisis de texturaRedes complejasPixelbusiness.industryNode (networking)Pattern recognitionProcessament d'imatgescomplex networksQA75.5-76.95Xarxes complexesComplex networkTexture analysisElectronic computers. Computer scienceComputer Science::Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareELCVIA: electronic letters on computer vision and image analysis
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Impact of the community structure on the dynamics of complex networks

2023

Networks are everywhere. We encounter them daily in our lives, through our social interactions, how we come up with decisions in our brain, to having phone calls, conducting financial transactions, and traveling from one place to another. Individual actions are influenced by their environment, which is, in turn, influenced by the network's topology. Notably, individuals may change their actions, ideas, or opinions to conform to the aspirations of a particular social group. In the same vein, the spread of a virus can take a certain course if the network's structure induces specific pathways for expansion. In such scenarios, communities substantially impact the evolution of the dynamics. They…

Diffusion[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]CommunautésRéseaux complexesCommunitiesComplex networksCentralityCentralitéTopologieInfluential nodesTopologyNœuds influents
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