Search results for "complex networks"

showing 10 items of 49 documents

Social Network Analysis of Sicilian Mafia Interconnections

2019

In this paper, we focus on the study of Sicilian Mafia organizations through Social Network Analysis. We analyse datasets reflecting two different Mafia Families, based on examinations of digital trails and judicial documents, respectively. The first dataset includes the phone calls logs among suspected individuals. The second one is based on police traces of meeting that have taken place among different types of criminals. Our breakthrough is twofold. First in the method followed to generate these new datasets. Second, in the method used to carry out a quantitative phenomena investigation that are hard to evaluate. Our networks are weighted ones, with each weight catching the frequency of …

Focus (computing)Settore INF/01 - InformaticaComputer scienceSocial network analysis (criminology)Complex networksGraph theoryComplex networkData scienceCriminal networkslanguage.human_languageGraph theoryPhoneTerrorismComplex networks; Criminal networks; Graph theory; Social Network AnalysislanguageSicilianSocial Network Analysis
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Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth

2020

Reconstructing a “forma mentis”, a mindset, and its changes, means capturing how individuals perceive topics, trends and experiences over time. To this aim we use forma mentis networks (FMNs), which enable direct, microscopic access to how individuals conceptually perceive knowledge and sentiment around a topic, providing richer contextual information than machine learning. FMNs build cognitive representations of stances through psycholinguistic tools like conceptual associations from semantic memory (free associations, i.e., one concept eliciting another) and affect norms (valence, i.e., how attractive a concept is). We test FMNs by investigating how Norwegian nursing and engineering stude…

General Computer ScienceEntropymedia_common.quotation_subjectClosenessComplex networksNetwork Science and Online Social NetworksMindset050105 experimental psychologyPsycholinguisticslcsh:QA75.5-76.95Education03 medical and health sciences0302 clinical medicineNursingLearning outcomesPerceptionOpenness to experienceStance detection0501 psychology and cognitive sciencesmedia_commonTeamworkPsycholinguisticsData Science05 social sciencesProfessional developmentHealthcareCognitionSTEMComputational LinguisticsAttitudeVDP::Medisinske Fag: 700::Helsefag: 800lcsh:Electronic computers. Computer science030217 neurology & neurosurgeryMindset modelling
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Filtering Real World Networks: A Correlation Analysis of Statistical Backbone Techniques

2023

Networks are an invaluable tool for representing and understanding complex systems. They offer a wide range of applications, including identifying crucial nodes, uncovering communities, and exploring network formation. However, when dealing with large networks, the computational challenge can be overwhelming. Fortunately, researchers have developed several techniques to address this issue by reducing network size while preserving its fundamental properties [1-9]. To achieve this goal, two main approaches have emerged: structural and statistical methods. Structural methods aim to keep a set of topological features of the network while reducing its size. In contrast, statistical methods elimi…

Graph SummarizationSparsificationBackbone Filtering TechniquesNetwork Compression[INFO] Computer Science [cs]Complex Networks
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NetBone: A Python Package for Extracting Backbones of Weighted Networks

2023

NetBone is a new open-source Python package designed to simplify analyzing complex networks. With a wide range of techniques available, Net-Bone allows researchers to extract the backbone of a network while preserving its essential structure. The package includes nine structural methods and five statistical techniques, offering users a comprehensive solution to network analysis. It is user-friendly and straightforward to use, with easy installation. The package accepts different types of inputs, including data frames or Networkx graphs, and provides evaluation measures for comparative purposes. Additionally, NetBone offers an option to generate plots. Its versatility makes it a valuable too…

Graph SummarizationSparsificationBackbone Filtering TechniquesNetwork Compression[INFO] Computer Science [cs]Complex Networks
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Image Segmentation by Deep Community Detection Approach

2017

International audience; To address the problem of segmenting an image into homogeneous communities this paper proposes an efficient algorithm to detect deep communities in the image by maximizing at each stage a new centrality measure, called the local Fiedler vector centrality (LFVC). This measure is associated with the sensitivity of algebraic connectivity to node removals. We show that a greedy node removal strategy, based on iterative maximization of LFVC, has bounded performance loss relative to the optimal, but intractable, combinatorial batch removal strategy. A remarkable feature of this method is the ability to segments the image automatically into homogeneous regions by maximizing…

Image segmentationAlgebraic connectivitybusiness.industrySegmentation-based object categorizationComputer scienceNode (networking)Complex networksScale-space segmentationLocal Fiedler vector centrality020206 networking & telecommunicationsPattern recognition02 engineering and technologyImage segmentation[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]Removal strategyFeature (computer vision)0202 electrical engineering electronic engineering information engineeringDeep community detection020201 artificial intelligence & image processingSegmentationArtificial intelligencebusinessCentrality
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Google matrix analysis of worldwide football mercato

2018

[EN] The worldwide football transfer market is analyzed as a directed complex network: the football clubs are the network nodes and the directed edges are weighted by the total amount of money transferred from a club to another. The Google matrix description allows to treat every club independently of their richness and allows to measure for a given club the efficiency of player sales and player acquisitions. The PageRank algorithm, developed initially for the World Wide Web, naturally characterizes the ability of a club to import players. The CheiRank algorithm, also developed to analyze large scale directed complex networks, characterizes the ability of a club to export players. The analy…

PageRankCheiRankComputer scienceBig dataComplex networksPLSFootballlaw.inventionWorld Wide WebBig dataCheiRankPageRanklawInternet dataQCAGoogle matrixMarkov chainsGoogle matrixMarkov chainbusiness.industryWeb dataComputingMilieux_PERSONALCOMPUTINGConferenceComplex networkFootball transfer marketSEMbusinessProceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)
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Wikipedia network analysis of cancer interactions and world influence

2019

AbstractWe apply the Google matrix algorithms for analysis of interactions and influence of 37 cancer types, 203 cancer drugs and 195 world countries using the network of 5 416 537 English Wikipedia articles with all their directed hyperlinks. The PageRank algorithm provides the importance order of cancers which has 60% and 70% overlaps with the top 10 cancers extracted from World Health Organization GLOBOCAN 2018 and Global Burden of Diseases Study 2017, respectively. The recently developed reduced Google matrix algorithm gives networks of interactions between cancers, drugs and countries taking into account all direct and indirect links between these selected 435 entities. These reduced n…

PageRankDatabases FactualComputer scienceSocial Sciences01 natural sciencesLung and Intrathoracic TumorsHematologic Cancers and Related Disorders0302 clinical medicineSociologyNeoplasmsBreast TumorsMedicine and Health SciencesComputingMilieux_MISCELLANEOUSNon-Hodgkin lymphoma0303 health sciencesMultidisciplinaryGoogle matrixApplied MathematicsSimulation and ModelingProstate Cancer[PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph]QRProstate DiseasesOnline EncyclopediasHematology[SDV.SP]Life Sciences [q-bio]/Pharmaceutical sciencesOvarian CancerOncology030220 oncology & carcinogenesisPhysical SciencesMedicineLymphomasCancersAlgorithmsNetwork analysisResearch ArticleScienceUrologyMEDLINEComplex networksAntineoplastic Agents[SDV.CAN]Life Sciences [q-bio]/CancerResearch and Analysis Methods[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]World Wide Web03 medical and health sciences0103 physical sciencesBreast CancerLeukemiasmedicineHumansMass Media010306 general physicsPagerank algorithm030304 developmental biologyGoogle matrixCancerCancers and NeoplasmsHyperlinkmedicine.diseaseData scienceCommunicationsGenitourinary Tract TumorsCancer drugsRankingEncyclopedias[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]Gynecological TumorsMathematicsWikipedia
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Unprecedented layered coordination polymers of dithiolene group 10 metals: Magnetic and electrical properties

2016

One-pot reactions between Ni(ii), Pd(ii) or Pt(ii) salts and 3,6-dichloro-1,2-benzenedithiol (HSC6H2Cl2SH) in KOH medium under argon lead to a series of bis-dithiolene coordination polymers. X-ray analysis shows the presence of a common square planar complex [M(SC6H2Cl2S)2]2- linked to potassium cations forming either a two-dimensional coordination polymer network for {[K2(μ-H2O)2(μ-thf)(thf)2][M(SC6H2Cl2S)2]}n [M = Ni (1) and Pd (2)] or a one-dimensional coordination polymer for {[K2(μ-H2O)2(thf)6][Pt(SC6H2Cl2S)2]}n (3). In 3 the coordination environment of the potassium ions may slightly change leading to the two-dimensional coordination polymer {[K2(μ-H2O)(μ-thf)2][Pt(SC6H2Cl2S)2]}n (4) …

Palladium compoundsCoordination polymerPolymersPotassiumInorganic chemistryComplex networkschemistry.chemical_elementX ray analysis010402 general chemistryPotassium ions01 natural sciencesInorganic Chemistrychemistry.chemical_compoundGroup (periodic table)NickelPlatinumchemistry.chemical_classificationArgon010405 organic chemistryPolymerQuímicaChlorine compounds0104 chemical sciencesCrystallographychemistryDiamagnetismComplexationCoordination reactions
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Backbone of credit relationships in the Japanese credit market

2016

We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversification of loans for banks and firms. Our investigation is done year by year and it covers more than thirty years during the period from 1980 to 2011. We relate some of our findings with economic events that have characterized the Japanese credit market during the last years. The study of the time evolution of the backbone allows us to detect changes occurred in network size,…

Physics - Physics and SocietyGeneral methodcredit marketeducationDiversification (finance)FOS: Physical sciencesNetwork sizePhysics and Society (physics.soc-ph)01 natural sciences010305 fluids & plasmasFOS: Economics and businesscomplex network0502 economics and business0103 physical sciencesEconometricsFraction (mathematics)050207 economicshealth care economics and organizations05 social sciencescomplex networksComplex networkSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)information filteringComputer Science ApplicationsComputational MathematicsModeling and SimulationBond marketstatistically validated networksBusinessGeneral Finance (q-fin.GN)Quantitative Finance - General FinanceNull hypothesisEPJ Data Science
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Exploratory analysis of safety data and their interrelation with flight trajectories and network metrics

2013

This paper presents an exploratory analysis of the correlation between different network metrics and safety events. The objective is to develop analysis methods and indicators that relate the network structure with safety events. In particular by using data gathered with an automatic tool developed by EUROCONTROL and a database of traffic data, we measured the correlations between the classical metrics of the airspace network, especially the network of navigation points, and the occurrence of Short Term Conflicts Alerts (STCA). The results obtained are not univocal; correlation between STCA occurrences and classical network metrics are present but the correlation coefficient is around 0.7. …

Safety Events STCA Aircraft Trajectories Complex NetworksSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)
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