{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:43:57Z","timestamp":1666010637762},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:p>We present clustAnalytics, an R package available now on CRAN, which provides methods to validate the results of clustering algorithms on unweighted and weighted networks, particularly for the cases where the existence of a community structure is unknown. clustAnalytics comprises a set of criteria for assessing the significance and stability of a clustering. To evaluate clusters\u2019 significance, clustAnalytics provides a set of community scoring functions, and systematically compares their values to those of a suitable null model. For this it employs a switching model to produce randomized graphs with weighted edges. To test for clusters\u2019 stability, a non parametric bootstrap method is used, together with similarity metrics derived from information theory and combinatorics. In order to assess the effectiveness of our clustering quality evaluation methods, we provide methods to synthetically generate networks (weighted or not) with a ground truth community structure based on the stochastic block model construction, as well as on a preferential attachment model, the latter producing networks with communities and scale-free degree distribution.<\/jats:p>","DOI":"10.3233\/faia220328","type":"book-chapter","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:25:16Z","timestamp":1666009516000},"source":"Crossref","is-referenced-by-count":0,"title":["The Assessment of Clustering on Weighted Network with R Package clustAnalytics"],"prefix":"10.3233","author":[{"given":"Argimiro","family":"Arratia","sequence":"first","affiliation":[{"name":"Soft Computing Research Group (SOCO) at Intelligent Data Science and Artificial Intelligence Research Center, Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Spain. argimiro@cs.upc.edu, marti.renedo@gmail.com"}]},{"given":"Mart\u00ed","family":"Renedo-Mirambell","sequence":"additional","affiliation":[{"name":"Soft Computing Research Group (SOCO) at Intelligent Data Science and Artificial Intelligence Research Center, Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Spain. argimiro@cs.upc.edu, marti.renedo@gmail.com"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:25:22Z","timestamp":1666009522000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220328","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]}}}