{"title":"Community detection in interval-weighted networks","authors":"Hélder Alves, Paula Brito, Pedro Campos","doi":"10.1007/s10618-023-00975-z","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"267 1","pages":"0"},"PeriodicalIF":2.8000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Mining and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10618-023-00975-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
期刊介绍：
Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.
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