Search results for " Computer Science"

showing 10 items of 3983 documents

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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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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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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Towards realistic artificial benchmark for community detection algorithms evaluation

2013

Many algorithms have been proposed for revealing the community structure in complex networks. Tests under a wide range of realistic conditions must be performed in order to select the most appropriate for a particular application. Artificially generated networks are often used for this purpose. The most realistic generative method to date has been proposed by Lancichinetti, Fortunato and Radicchi (LFR). However, it does not produce networks with some typical features of real-world networks. To overcome this drawback, we investigate two alternative modifications of this algorithm. Experimental results show that in both cases, centralisation and degree correlation values of generated networks…

FOS: Computer and information sciencesPhysics - Physics and Societypreferential attachmentComputer Networks and CommunicationsComputer science[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]FOS: Physical sciencesvirtual communitiesPhysics and Society (physics.soc-ph)01 natural sciences010305 fluids & plasmasEducation0103 physical sciencescommunity detectionbenchmarking010306 general physicsSocial and Information Networks (cs.SI)CommunicationComputer Science - Social and Information Networkscomplex networksweb based communitiesonline communitiesconfiguration modellingIdentification (information)LFR benchmarkBenchmark (computing)[ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH]community structureAlgorithmtopological propertiesSoftware
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Security of public key cryptosystems based on Chebyshev Polynomials

2004

Chebyshev polynomials have been recently proposed for designing public-key systems. Indeed, they enjoy some nice chaotic properties, which seem to be suitable for use in Cryptography. Moreover, they satisfy a semi-group property, which makes possible implementing a trapdoor mechanism. In this paper we study a public key cryptosystem based on such polynomials, which provides both encryption and digital signature. The cryptosystem works on real numbers and is quite efficient. Unfortunately, from our analysis it comes up that it is not secure. We describe an attack which permits to recover the corresponding plaintext from a given ciphertext. The same attack can be applied to produce forgeries …

FOS: Computer and information sciencesPlaintext-aware encryptionTheoretical computer scienceComputer Science - Cryptography and SecurityCramer–Shoup cryptosystemData_CODINGANDINFORMATIONTHEORYDeterministic encryptionHybrid cryptosystemCryptosystemElectrical and Electronic EngineeringSemantic securityThreshold cryptosystemCryptography and Security (cs.CR)Goldwasser–Micali cryptosystemMathematics
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On prefix normal words and prefix normal forms

2016

A $1$-prefix normal word is a binary word with the property that no factor has more $1$s than the prefix of the same length; a $0$-prefix normal word is defined analogously. These words arise in the context of indexed binary jumbled pattern matching, where the aim is to decide whether a word has a factor with a given number of $1$s and $0$s (a given Parikh vector). Each binary word has an associated set of Parikh vectors of the factors of the word. Using prefix normal words, we provide a characterization of the equivalence class of binary words having the same set of Parikh vectors of their factors. We prove that the language of prefix normal words is not context-free and is strictly contai…

FOS: Computer and information sciencesPrefix codePrefix normal wordPre-necklaceDiscrete Mathematics (cs.DM)General Computer ScienceFormal Languages and Automata Theory (cs.FL)Binary numberComputer Science - Formal Languages and Automata TheoryContext (language use)Binary languageLyndon words0102 computer and information sciences02 engineering and technologyPrefix grammarprefix normal formsKraft's inequalityCharacterization (mathematics)Lyndon word01 natural sciencesPrefix normal formenumerationTheoretical Computer ScienceFOS: Mathematics0202 electrical engineering electronic engineering information engineeringMathematics - CombinatoricsMathematicsDiscrete mathematicsprefix normal words prefix normal forms binary languages binary jumbled pattern matching pre-necklaces Lyndon words enumerationbinary jumbled pattern matchingSettore INF/01 - InformaticaComputer Science (all)pre-necklacesComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)prefix normal wordsPrefix010201 computation theory & mathematics020201 artificial intelligence & image processingCombinatorics (math.CO)binary languagesComputer Science::Formal Languages and Automata TheoryWord (group theory)Computer Science - Discrete MathematicsTheoretical Computer Science
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Primitive sets of words

2020

Given a (finite or infinite) subset $X$ of the free monoid $A^*$ over a finite alphabet $A$, the rank of $X$ is the minimal cardinality of a set $F$ such that $X \subseteq F^*$. We say that a submonoid $M$ generated by $k$ elements of $A^*$ is {\em $k$-maximal} if there does not exist another submonoid generated by at most $k$ words containing $M$. We call a set $X \subseteq A^*$ {\em primitive} if it is the basis of a $|X|$-maximal submonoid. This definition encompasses the notion of primitive word -- in fact, $\{w\}$ is a primitive set if and only if $w$ is a primitive word. By definition, for any set $X$, there exists a primitive set $Y$ such that $X \subseteq Y^*$. We therefore call $Y$…

FOS: Computer and information sciencesPrimitive setDiscrete Mathematics (cs.DM)General Computer ScienceFormal Languages and Automata Theory (cs.FL)Pseudo-repetitionComputer Science - Formal Languages and Automata Theory0102 computer and information sciences02 engineering and technology01 natural sciencesTheoretical Computer ScienceCombinatoricsCardinalityFree monoidBi-rootFOS: Mathematics0202 electrical engineering electronic engineering information engineeringMathematics - CombinatoricsRank (graph theory)Primitive root modulo nMathematicsHidden repetitionSettore INF/01 - InformaticaIntersection (set theory)k-maximal monoidFunction (mathematics)Basis (universal algebra)010201 computation theory & mathematics020201 artificial intelligence & image processingCombinatorics (math.CO)Computer Science::Formal Languages and Automata TheoryWord (group theory)Computer Science - Discrete Mathematics
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Application of LEAN Principles to Improve Business Processes: a Case Study in a Latvian IT Company

2020

The research deals with application of the LEAN principles to business processes of a typical IT company. The paper discusses LEAN principles amplifying advantages and shortcomings of their application. The authors suggest use of the LEAN principles as a tool to identify improvement potential for IT company's business processes and work-flow efficiency. During a case study the implementation of LEAN principles has been exemplified in business processes of a particular Latvian IT company. The obtained results and conclusions can be used for meaningful and successful application of LEAN principles and methods in projects of other IT companies.

FOS: Computer and information sciencesProcess managementGeneral Computer ScienceBusiness processLatvianDatabases (cs.DB)language.human_languageSoftware Engineering (cs.SE)Computer Science - Software EngineeringComputer Science - Computers and SocietyComputer Science - DatabasesComputers and Society (cs.CY)languageBusinessBaltic Journal of Modern Computing
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Structural bias in population-based algorithms

2014

Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a ‘fitness function’ specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure eff…

FOS: Computer and information sciencesQA75Mathematical optimizationInformation Systems and ManagementPopulation-based algorithmsFitness landscapemedia_common.quotation_subjectPopulationStructural biasEvolutionary computationPopulation-based algorithmEvolutionary computationTheoretical Computer ScienceArtificial IntelligenceBlack boxEconometricsQuality (business)OptimisationAlgorithmic designNeural and Evolutionary Computing (cs.NE)educationMathematicsmedia_commonta113education.field_of_studyFitness functionPopulation sizeComputer Science - Neural and Evolutionary ComputingComputer Science ApplicationsControl and Systems EngineeringAlgorithmSoftwarePopulation variance
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