{"title":"Simbanex:基于相似性的 IEEE VIS 出版物探索","authors":"Daniel Witschard, Ilir Jusufi, Andreas Kerren","doi":"arxiv-2409.00478","DOIUrl":null,"url":null,"abstract":"Embeddings are powerful tools for transforming complex and unstructured data\ninto numeric formats suitable for computational analysis tasks. In this work,\nwe use multiple embeddings for similarity calculations to be applied in\nbibliometrics and scientometrics. We build a multivariate network (MVN) from a\nlarge set of scientific publications and explore an aspect-driven analysis\napproach to reveal similarity patterns in the given publication data. By\ndividing our MVN into separately embeddable aspects, we are able to obtain a\nflexible vector representation which we use as input to a novel method of\nsimilarity-based clustering. Based on these preprocessing steps, we developed a\nvisual analytics application, called Simbanex, that has been designed for the\ninteractive visual exploration of similarity patterns within the underlying\npublications.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"307 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simbanex: Similarity-based Exploration of IEEE VIS Publications\",\"authors\":\"Daniel Witschard, Ilir Jusufi, Andreas Kerren\",\"doi\":\"arxiv-2409.00478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Embeddings are powerful tools for transforming complex and unstructured data\\ninto numeric formats suitable for computational analysis tasks. In this work,\\nwe use multiple embeddings for similarity calculations to be applied in\\nbibliometrics and scientometrics. We build a multivariate network (MVN) from a\\nlarge set of scientific publications and explore an aspect-driven analysis\\napproach to reveal similarity patterns in the given publication data. By\\ndividing our MVN into separately embeddable aspects, we are able to obtain a\\nflexible vector representation which we use as input to a novel method of\\nsimilarity-based clustering. Based on these preprocessing steps, we developed a\\nvisual analytics application, called Simbanex, that has been designed for the\\ninteractive visual exploration of similarity patterns within the underlying\\npublications.\",\"PeriodicalId\":501285,\"journal\":{\"name\":\"arXiv - CS - Digital Libraries\",\"volume\":\"307 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00478\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simbanex: Similarity-based Exploration of IEEE VIS Publications
Embeddings are powerful tools for transforming complex and unstructured data
into numeric formats suitable for computational analysis tasks. In this work,
we use multiple embeddings for similarity calculations to be applied in
bibliometrics and scientometrics. We build a multivariate network (MVN) from a
large set of scientific publications and explore an aspect-driven analysis
approach to reveal similarity patterns in the given publication data. By
dividing our MVN into separately embeddable aspects, we are able to obtain a
flexible vector representation which we use as input to a novel method of
similarity-based clustering. Based on these preprocessing steps, we developed a
visual analytics application, called Simbanex, that has been designed for the
interactive visual exploration of similarity patterns within the underlying
publications.