Search results for "PLASMA"

showing 10 items of 4043 documents

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

2017

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…

FOS: Computer and information sciencesInformation transferComputer scienceGaussianSocial SciencesGeneral Physics and AstronomyInformation theory01 natural sciences010305 fluids & plasmasState spaceStatistical physicslcsh:Scienceinformation theorymultiscale entropylcsh:QC1-999Interaction informationMathematics and Statisticssymbolsinformation dynamicsInformation dynamics; Information transfer; Multiscale entropy; Multivariate time series analysis; Redundancy and synergy; State space models; Vector autoregressive models; Physics and Astronomy (all)information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive modelsMultivariate time series analysiMathematics - Statistics Theorylcsh:AstrophysicsStatistics Theory (math.ST)Statistics - ApplicationsMethodology (stat.ME)symbols.namesakePhysics and Astronomy (all)0103 physical scienceslcsh:QB460-466FOS: Mathematicsinformation transferRelevance (information retrieval)Applications (stat.AP)Transfer Entropy010306 general physicsGaussian processStatistics - MethodologyState space modelstate space modelsmultivariate time series analysisredundancy and synergyvector autoregressive modelsInformation dynamicVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaTransfer entropylcsh:Qlcsh:PhysicsEntropy
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Local Granger causality

2021

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …

FOS: Computer and information sciencesInformation transferGaussianFOS: Physical sciencestechniques; information theory; granger causalityMachine Learning (stat.ML)Quantitative Biology - Quantitative Methods01 natural sciences010305 fluids & plasmasVector autoregressionsymbols.namesakegranger causalityGranger causalityStatistics - Machine Learning0103 physical sciencesApplied mathematicstime serie010306 general physicsQuantitative Methods (q-bio.QM)Mathematicsinformation theoryStochastic processDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)Discrete time and continuous timeAutoregressive modelFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsTransfer entropytechniquesPhysics - Computational Physics
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The network of global corporate control.

2011

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic “super-entity” that raises new important issues both for researchers and policy make…

FOS: Computer and information sciencesInternationalityFinancial ManagementEconomicsFinancial intermediarylcsh:MedicineNetwork theorySocial and Behavioral Sciences01 natural sciences010305 fluids & plasmasMicroeconomics050207 economicslcsh:ScienceMutual fundIndustrial organizationProfessional CorporationsMultidisciplinaryCorporate governanceApplied MathematicsPhysics05 social sciencesCommerceComputer Science - Social and Information NetworksComplex SystemsSocial Control PoliciesCore (game theory)Interdisciplinary PhysicsGeneral Finance (q-fin.GN)Quantitative Finance - General FinanceResearch ArticlePhysics - Physics and SocietyControl (management)FOS: Physical sciencesSpatial Economic AnalysisPhysics and Society (physics.soc-ph)BiologyStatistical MechanicsFOS: Economics and businessFinancial management0502 economics and business0103 physical sciencesownership corporate control network theoryStructure of MarketsSocial and Information Networks (cs.SI)business.industryFinancial marketlcsh:RIndustrial Organizationlcsh:QbusinessMathematicsPloS one
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Online shortest paths with confidence intervals for routing in a time varying random network

2018

International audience; The increase in the world's population and rising standards of living is leading to an ever-increasing number of vehicles on the roads, and with it ever-increasing difficulties in traffic management. This traffic management in transport networks can be clearly optimized by using information and communication technologies referred as Intelligent Transport Systems (ITS). This management problem is usually reformulated as finding the shortest path in a time varying random graph. In this article, an online shortest path computation using stochastic gradient descent is proposed. This routing algorithm for ITS traffic management is based on the online Frank-Wolfe approach.…

FOS: Computer and information sciencesMathematical optimizationComputer sciencePopulation02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE][INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[SPI]Engineering Sciences [physics][INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]0502 economics and business11. SustainabilityComputer Science - Data Structures and Algorithms0202 electrical engineering electronic engineering information engineeringFOS: MathematicsData Structures and Algorithms (cs.DS)educationIntelligent transportation systemMathematics - Optimization and ControlRandom graph050210 logistics & transportationeducation.field_of_studyStochastic process[SPI.PLASMA]Engineering Sciences [physics]/Plasmas05 social sciencesApproximation algorithm[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationStochastic gradient descentOptimization and Control (math.OC)[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Shortest path problem020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Routing (electronic design automation)[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]
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PageRank model of opinion formation on Ulam networks

2013

We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks have certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.

FOS: Computer and information sciencesPageRankPhysics - Physics and SocietyTheoretical computer scienceSznajd model[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]FOS: Physical sciencesGeneral Physics and AstronomyNetwork structurePhysics and Society (physics.soc-ph)[ PHYS.PHYS.PHYS-SOC-PH ] Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph]01 natural sciencesopinion formation010305 fluids & plasmaslaw.inventionPageRanklawIntermittency0103 physical sciencesAlgebraic number010306 general physicsSocial and Information Networks (cs.SI)Physicsvoting models[PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph]Frame (networking)Process (computing)Computer Science - Social and Information NetworksNonlinear Sciences - Chaotic Dynamics[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]Chaotic Dynamics (nlin.CD)Opinion formation
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Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data

2014

Big data open up unprecedented opportunities to investigate complex systems including the society. In particular, communication data serve as major sources for computational social sciences but they have to be cleaned and filtered as they may contain spurious information due to recording errors as well as interactions, like commercial and marketing activities, not directly related to the social network. The network constructed from communication data can only be considered as a proxy for the network of social relationships. Here we apply a systematic method, based on multiple hypothesis testing, to statistically validate the links and then construct the corresponding Bonferroni network, gen…

FOS: Computer and information sciencesPhysics - Physics and SocietyBig dataFOS: Physical sciencesGeneral Physics and AstronomyPhysics and Society (physics.soc-ph)computer.software_genre01 natural sciences010305 fluids & plasmassymbols.namesake0103 physical sciences010306 general physicsProxy (statistics)Social and Information Networks (cs.SI)PhysicsSocial networkbusiness.industryComputer Science - Social and Information NetworksComplex networkcomplex networks social systems statistically validated networks mobile call records 3-motifsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Bonferroni correctionMobile phonesymbolsMobile telephonyData miningRaw databusinesscomputer
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Epidemic spreading and aging in temporal networks with memory

2018

Time-varying network topologies can deeply influence dynamical processes mediated by them. Memory effects in the pattern of interactions among individuals are also known to affect how diffusive and spreading phenomena take place. In this paper we analyze the combined effect of these two ingredients on epidemic dynamics on networks. We study the susceptible-infected-susceptible (SIS) and the susceptible-infected-removed (SIR) models on the recently introduced activity-driven networks with memory. By means of an activity-based mean-field approach we derive, in the long time limit, analytical predictions for the epidemic threshold as a function of the parameters describing the distribution of …

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceAnalytical predictionsEpidemic dynamicsFOS: Physical sciencesPhysics and Society (physics.soc-ph)Network topology01 natural sciences010305 fluids & plasmasNetworks and Complex Systems0103 physical sciencesQuantitative Biology::Populations and EvolutionStatistical physicsLimit (mathematics)010306 general physicsQuantitative Biology - Populations and EvolutionEpidemic controlSocial and Information Networks (cs.SI)Populations and Evolution (q-bio.PE)Computer Science - Social and Information NetworksFunction (mathematics)Computer Science::Social and Information NetworksArticlesDynamic modelsEpidemic thresholdEpidemic spreadingFOS: Biological sciencesMean field approachPhysical Review. E
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Elites, communities and the limited benefits of mentorship in electronic music

2020

AbstractWhile the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry.…

FOS: Computer and information sciencesPhysics - Physics and SocietyLongitudinal dataFOS: Physical scienceslcsh:MedicinePhysics and Society (physics.soc-ph)Musical01 natural sciencesArticle010305 fluids & plasmasMentorshipElectronic music0103 physical sciencesSociology010306 general physicslcsh:ScienceSocial and Information Networks (cs.SI)Multidisciplinarysocial physics complex networksComputational sciencelcsh:RMedia studiesScientific dataComputer Science - Social and Information NetworksPopularitySettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Applied physicsRankingElitelcsh:QScientific Reports
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An Empirical Study of the Relation Between Community Structure and Transitivity

2012

One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and loosely interconnected groups of nodes called communities. In an attempt to better understand this concept, we study the relationship between the strength of the community structure and the network transitivity (or clustering coefficient). Although intuitively appealing, this analysis was not performed before. We adopt an approach based on random models to empirically study how one property varies depending on the other. It turns out the transitivity increases with the community structure strength, and is also affected by the distribution of the community sizes. Furthermore, …

FOS: Computer and information sciencesPhysics - Physics and SocietyProperty (philosophy)FOS: Physical sciencesPhysics and Society (physics.soc-ph)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciencesComplex NetworksClustering010305 fluids & plasmasEmpirical research0103 physical sciences010306 general physicstransitivityCommunity StructureClustering coefficientMathematicsSocial and Information Networks (cs.SI)Transitive relationCommunity structure[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science - Social and Information NetworksComplex networkDegree distributionZero (linguistics)Mathematical economics
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Extracting Backbones in Weighted Modular Complex Networks

2020

AbstractNetwork science provides effective tools to model and analyze complex systems. However, the increasing size of real-world networks becomes a major hurdle in order to understand their structure and topological features. Therefore, mapping the original network into a smaller one while preserving its information is an important issue. Extracting the so-called backbone of a network is a very challenging problem that is generally handled either by coarse-graining or filter-based methods. Coarse-graining methods reduce the network size by grouping similar nodes, while filter-based methods prune the network by discarding nodes or edges based on a statistical property. In this paper, we pro…

FOS: Computer and information sciencesPhysics - Physics and SocietyTheoretical computer scienceComputer scienceMathematics and computingComplex systemComplex networkslcsh:MedicineFOS: Physical sciencesNetwork science02 engineering and technologyPhysics and Society (physics.soc-ph)[INFO] Computer Science [cs]01 natural sciencesArticle010305 fluids & plasmasSet (abstract data type)020204 information systems0103 physical sciences0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]lcsh:ScienceAuthor CorrectionComputingMilieux_MISCELLANEOUSConnected componentSocial and Information Networks (cs.SI)Multidisciplinarybusiness.industryPhysicslcsh:RCommunity structureComputer Science - Social and Information NetworksComplex networkModular designlcsh:Qbusiness
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