{"title":"clusteranalytics:一个用于评估网络中社区稳定性和重要性的R包","authors":"Martí Renedo-Mirambell, Argimiro Arratia","doi":"10.32614/rj-2023-057","DOIUrl":null,"url":null,"abstract":"This paper introduces the R package [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics), which comprises a set of criteria for assessing the significance and stability of communities in networks found by any clustering algorithm. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) works with graphs of class [igraph](https://CRAN.R-project.org/package=igraph) from the R-package [igraph](https://CRAN.R-project.org/package=igraph), extended to handle weighted and/or directed graphs. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) provides a set of community scoring functions, and methods to systematically compare their values to those of a suitable null model, which are of use when testing for cluster significance. It also provides a non parametric bootstrap method combined with similarity metrics derived from information theory and combinatorics, useful when testing for cluster stability, as well as a method to synthetically generate a weighted network with a ground truth community structure based on the preferential attachment model construction, producing networks with communities and scale-free degree distribution.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"110 1-2","pages":"0"},"PeriodicalIF":2.3000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"clustAnalytics: An R Package for Assessing Stability and Significance of Communities in Networks\",\"authors\":\"Martí Renedo-Mirambell, Argimiro Arratia\",\"doi\":\"10.32614/rj-2023-057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces the R package [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics), which comprises a set of criteria for assessing the significance and stability of communities in networks found by any clustering algorithm. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) works with graphs of class [igraph](https://CRAN.R-project.org/package=igraph) from the R-package [igraph](https://CRAN.R-project.org/package=igraph), extended to handle weighted and/or directed graphs. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) provides a set of community scoring functions, and methods to systematically compare their values to those of a suitable null model, which are of use when testing for cluster significance. It also provides a non parametric bootstrap method combined with similarity metrics derived from information theory and combinatorics, useful when testing for cluster stability, as well as a method to synthetically generate a weighted network with a ground truth community structure based on the preferential attachment model construction, producing networks with communities and scale-free degree distribution.\",\"PeriodicalId\":51285,\"journal\":{\"name\":\"R Journal\",\"volume\":\"110 1-2\",\"pages\":\"0\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2023-057\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2023-057","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
clustAnalytics: An R Package for Assessing Stability and Significance of Communities in Networks
This paper introduces the R package [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics), which comprises a set of criteria for assessing the significance and stability of communities in networks found by any clustering algorithm. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) works with graphs of class [igraph](https://CRAN.R-project.org/package=igraph) from the R-package [igraph](https://CRAN.R-project.org/package=igraph), extended to handle weighted and/or directed graphs. [clustAnalytics](https://CRAN.R-project.org/package=clustAnalytics) provides a set of community scoring functions, and methods to systematically compare their values to those of a suitable null model, which are of use when testing for cluster significance. It also provides a non parametric bootstrap method combined with similarity metrics derived from information theory and combinatorics, useful when testing for cluster stability, as well as a method to synthetically generate a weighted network with a ground truth community structure based on the preferential attachment model construction, producing networks with communities and scale-free degree distribution.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.