0000000000653790

AUTHOR

Alexander Veremyev

showing 4 related works from this author

Exploring social media network landscape of post-Soviet space

2019

The “post-Soviet space” consists of countries with a substantial fraction of the world’s population; however, unlike many other regions, its social media network landscape is still somewhat under-explored. This paper aims at filling this gap. To this purpose, we use anonymized data on user friendships at VK.com (also known as VKontakte and, informally, as “Russian Facebook”), which is the largest and most popular social media portal in the post-Soviet space with hundreds of millions of user accounts. Using the VK network snapshots from October 2015 to December 2016, we conduct a “multiscale” empirical study of this network by considering conn…

General Computer SciencePopulationsosiaalinen mediaContext (language use)010501 environmental sciencesSpace (commercial competition)01 natural sciencesEmpirical researchbig data0502 economics and businessSocial network servicesGeneral Materials ScienceSocial mediaEconomic geographyeducationsovellukset (tietotekniikka)0105 earth and related environmental sciencesta113verkostoteducation.field_of_studyModularity (networks)big data applicationsData collection05 social sciencesGeneral Engineeringnetwork theory (graphs)Scale (social sciences)lcsh:Electrical engineering. Electronics. Nuclear engineeringsocial network serviceslcsh:TK1-9971050203 business & management
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Graph-based exploration and clustering analysis of semantic spaces

2019

Abstract The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Specifically, we construct semantic networks based on word2vec representation of words, which is “learnt” from large text corpora (Google news, Amazon reviews), and “human built” word networks derived from the well-known lexical databases: WordNet and Moby Thesaurus. We compare “global” (e.g., degrees, distances, clustering coefficients) and “local” (e.g., most central nodes and community-type dense clusters) characteristics of considered networks. Our observations suggest that …

Text corpusSemantic spacesComputer Networks and CommunicationsComputer sciencegraph theory0211 other engineering and technologiesWordNetNetwork science02 engineering and technologysemanttinen webSemantic networkword2vec similarity networksWord2vec similarity networksClique relaxationscohesive clusters0202 electrical engineering electronic engineering information engineeringWord2vecCluster analysisThesaurus (information retrieval)021103 operations researchMultidisciplinaryInformation retrievalverkkoteorialcsh:T57-57.97Graph theorycliquesGraph theoryclique relaxationsComputational MathematicsCliqueslcsh:Applied mathematics. Quantitative methodssemantic spaces020201 artificial intelligence & image processingCohesive clusters
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Analysis of Viral Advertisement Re-Posting Activity in Social Media

2016

More and more businesses use social media to advertise their services. Such businesses typically maintain online social network accounts and regularly update their pages with advertisement messages describing new products and promotions. One recent trend in such businesses’ activity is to offer incentives to individual users for re-posting the advertisement messages to their own profiles, thus making it visible to more and more users. A common type of an incentive puts all the re-posting users into a random draw for a valuable gift. Understanding the dynamics of user engagement into the re-posting activity can shed light on social influence mechanisms and help determine the optimal incentiv…

Value (ethics)IncentiveUser engagementSocial networkbusiness.industryInternet privacySocial mediaAdvertisingBusinessInformation cascadeSocial influence
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Network-based indices of individual and collective advising impacts in mathematics

2020

AbstractAdvising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can potentially be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual, but also subsequent academic advising records of those students. We also define and calculate the extensions of the proposed indices that account for student co-advising (referred to as “adjusted a-indices”). In addition, we extend some of the proposed metrics to ranking universities a…

social networksKnowledge managementAcademic advisingTrack (rail transport)01 natural sciencesSocial networkslcsh:QA75.5-76.95010305 fluids & plasmas03 medical and health sciencesBig datasosiaaliset verkostotbig data0103 physical sciencesComputingMilieux_COMPUTERSANDEDUCATIONAcademic professionMathematics Genealogy Project030304 developmental biology0303 health sciencesindeksitSocial networklcsh:T58.5-58.64business.industrylcsh:Information technologyScientific advising impactscientific advising impactComputer Science ApplicationsHuman-Computer InteractionRankingModeling and Simulationa-Indicesmathematics genealogy projectlcsh:Electronic computers. Computer sciencebusinessInformation SystemsComputational Social Networks
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