Search results for "Complex."

showing 10 items of 5824 documents

Quantum lower bound for inverting a permutation with advice

2014

Given a random permutation $f: [N] \to [N]$ as a black box and $y \in [N]$, we want to output $x = f^{-1}(y)$. Supplementary to our input, we are given classical advice in the form of a pre-computed data structure; this advice can depend on the permutation but \emph{not} on the input $y$. Classically, there is a data structure of size $\tilde{O}(S)$ and an algorithm that with the help of the data structure, given $f(x)$, can invert $f$ in time $\tilde{O}(T)$, for every choice of parameters $S$, $T$, such that $S\cdot T \ge N$. We prove a quantum lower bound of $T^2\cdot S \ge \tilde{\Omega}(\epsilon N)$ for quantum algorithms that invert a random permutation $f$ on an $\epsilon$ fraction of…

FOS: Computer and information sciencesNuclear and High Energy PhysicsComputer Science - Cryptography and SecurityGeneral Physics and AstronomyFOS: Physical sciencesOne-way functionComputational Complexity (cs.CC)Upper and lower boundsTheoretical Computer ScienceCyclic permutationCombinatoricsPermutationMathematical PhysicsMathematicsDiscrete mathematicsQuantum PhysicsBit-reversal permutationStatistical and Nonlinear PhysicsRandom permutationComputer Science - Computational ComplexityComputational Theory and MathematicsQuantum algorithmQuantum Physics (quant-ph)Advice (complexity)Cryptography and Security (cs.CR)MathematicsofComputing_DISCRETEMATHEMATICS
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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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A comparative analysis of the statistical properties of large mobile phone calling networks.

2014

Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network. If only reciprocal calls are considered, we obtain an undirected mutual calling network. The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges. We perform a comparative analysis of the statistical properties of these four networks, which are constructed from …

FOS: Computer and information sciencesPhysics - Physics and SocietyChinaComputer scienceFOS: Physical sciencesInformation Storage and RetrievalPhysics and Society (physics.soc-ph)ArticleSocial NetworkingComputer Communication NetworksSocio-technical systemsComputer SimulationProxy (statistics)Human communicationStatisticSocial and Information Networks (cs.SI)MultidisciplinaryModels StatisticalSocial networkbusiness.industryStatistical physicComputer Science - Social and Information NetworksNonlinear phenomenaComplex networkSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Mobile phonebusinessTelecommunicationsCell PhoneScientific reports
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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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Qualitative Comparison of Community Detection Algorithms

2011

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis r…

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciences02 engineering and technologyPhysics and Society (physics.soc-ph)Similarity measure[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Complex NetworksField (computer science)Qualitative analysis020204 information systems0202 electrical engineering electronic engineering information engineeringSocial and Information Networks (cs.SI)Algorithms ComparisonArtificial networks[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science - Social and Information Networks[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Complex networkPartition (database)Community Properties020201 artificial intelligence & image processingAlgorithmCommunity Detection
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Detecting informative higher-order interactions in statistically validated hypergraphs

2021

Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where hyperlinks encode higher-order interactions among a group of nodes. In spite of the large number of works on networks, highlighting informative hyperlinks in hypergraphs obtained from real world data is still an open problem. Here we propose an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections. We apply…

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceQC1-999Open problemFOS: Physical sciencesGeneral Physics and AstronomyPhysics and Society (physics.soc-ph)Astrophysicscomputer.software_genreENCODEMethodology (stat.ME)Statistics - MethodologySocial and Information Networks (cs.SI)PhysicsComputer Science - Social and Information NetworksFilter (signal processing)HyperlinkClass (biology)Settore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)QB460-466Pairwise comparisonData miningNoise (video)Null hypothesiscomputerhigher order interactions statistical validation complex networksCommunications Physics
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Whom to befriend to influence people

2020

Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person $v$ in the network has a certain threshold $t(v)$ for {\em activation}, i.e adoption of the product or idea. If $v$ has at least $t(v)$ activated neighbors, then $v$ will also become activated. If Alice wants to activate the entire social network, whom should she befriend? More generally, we study the problem of finding the minimum number of links that a set of external influencers should form to people in the network, in order to activate the entire social network. This {\em Minimum Links} Problem has applications in viral marketing and the study of epidemics. Its solution can be…

FOS: Computer and information sciencesPhysics - Physics and SocietyGeneral Computer ScienceFOS: Physical sciencesPhysics and Society (physics.soc-ph)0102 computer and information sciences02 engineering and technology01 natural sciencesSocial networksGraphTheoretical Computer ScienceCombinatoricsComputer Science - Data Structures and AlgorithmsGreedy algorithmFOS: Mathematics0202 electrical engineering electronic engineering information engineeringMathematics - CombinatoricsData Structures and Algorithms (cs.DS)Greedy algorithmTime complexityNP-completeMathematicsSocial and Information Networks (cs.SI)Social networkDiscrete mathematicsBinary treeDegree (graph theory)Computer Science (all)Order (ring theory)Computer Science - Social and Information NetworksJoin (topology)Influence maximizationGreedy algorithms010201 computation theory & mathematicsGraphs; Greedy algorithms; Influence maximization; NP-complete; Social networksProduct (mathematics)020201 artificial intelligence & image processingCombinatorics (math.CO)Constant (mathematics)GraphsTheoretical Computer Science
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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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