Search results for "centrality measures"

showing 4 items of 4 documents

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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Analyzing the Correlation of Classical and Community-aware Centrality Measures in Complex Networks

2021

International audience; Identifying influential nodes in social networks is a fundamental issue. Indeed, it has many applications, such as inhibiting epidemic spreading, accelerating information diffusion, preventing terrorist attacks, and much more. Classically, centrality measures quantify the node's importance based on various topological properties of the network, such as Degree and Betweenness. Nonetheless, these measures are agnostic of the community structure, although it is a ubiquitous characteristic encountered in many real-world networks. To overcome this drawback, there is a growing trend to design so-called community-aware centrality measures. Although several works investigate…

AssortativityTransitivityEfficiency) and nine mesoscopic topological features (MixingAverage Distance[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI]Density[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][INFO] Computer Science [cs][INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Diameter[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Influential NodesCentrality Measures[INFO]Computer Science [cs]Community StructureComputingMilieux_MISCELLANEOUS
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Correlation Analysis of Node and Edge Centrality Measures in Artificial Complex Networks

2021

The role of an actor in a social network is identified through a set of measures called centrality. Degree centrality, betweenness centrality, closeness centrality, and clustering coefficient are the most frequently used metrics to compute the node centrality. Their computational complexity in some cases makes unfeasible, when not practically impossible, their computations. For this reason, we focused on two alternative measures, WERW-Kpath and Game of Thieves, which are at the same time highly descriptive and computationally affordable. Our experiments show that a strong correlation exists between WERW-Kpath and Game of Thieves and the classical centrality measures. This may suggest the po…

Theoretical computer scienceSettore INF/01 - InformaticaComputational complexity theorySocial networkComputer sciencebusiness.industryNode (networking)Complex networksComplex networkSocial network analysisK-pathBetweenness centralityCentrality measuresCorrelation coefficientsCentralitybusinessSocial network analysisClustering coefficient
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Novel Version of PageRank, CheiRank and 2DRank for Wikipedia in Multilingual Network Using Social Impact

2020

International audience; Nowadays, information describing navigation behaviour of internet users are used in several fields, e-commerce, economy, sociology and data science. Such information can be extracted from different knowledge bases, including business-oriented ones. In this paper, we propose a new model for the PageRank, CheiRank and 2DRank algorithm based on the use of clickstream and pageviews data in the google matrix construction. We used data from Wikipedia and analysed links between over 20 million articles from 11 language editions. We extracted over 1.4 billion source-destination pairs of articles from SQL dumps and more than 700 million pairs from XML dumps. Additionally, we …

CheiRankPageRankSQLComputer sciencecomputer.internet_protocol01 natural sciences[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]010305 fluids & plasmaslaw.inventionCheiRankPageRanklaw0103 physical sciencesCentrality measures010306 general physicsClickstreamcomputer.programming_languageInformation retrievalGoogle matrixGoogle matrixPageviewsSocial impactPage viewcomputerClickstreamXMLWikipedia
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