{"data":{"id":"10.4121/0b40e329-7b33-4b8f-a289-ab6d1893b437","type":"dois","attributes":{"doi":"10.4121/0b40e329-7b33-4b8f-a289-ab6d1893b437","prefix":"10.4121","suffix":"0b40e329-7b33-4b8f-a289-ab6d1893b437","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Touwen, Lourens","givenName":"Lourens","familyName":"Touwen","affiliation":[],"nameIdentifiers":[]},{"name":"Bucur, Doina","givenName":"Doina","familyName":"Bucur","affiliation":[],"nameIdentifiers":[]},{"name":"van der Hofstad, Remco","nameType":"Personal","givenName":"Remco","familyName":"van der Hofstad","nameIdentifiers":[{"nameIdentifier":"0000-0003-1331-9697","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Litvak, Nelly","nameType":"Personal","givenName":"Nelly","familyName":"Litvak","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"0000-0002-6750-3484","nameIdentifierScheme":"https://orcid.org/"}]}],"titles":[{"title":"Data underlying the publication: Learning the mechanisms of network growth"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2023,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Statistics","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0104"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Applied Mathematics","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0102"},{"subject":"Other Physical Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0299"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information Systems","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0806"},{"subject":"Physical Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"02"},{"subject":"Mathematical Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"01"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"synthetic networks"},{"subject":"preferential attachment"},{"subject":"networks with fitness"},{"subject":"networks with aging"},{"subject":"uniform attachment"},{"subject":"machine learning"},{"subject":"model selection for complex networks"},{"subject":"complex networks"}],"contributors":[{"name":"TU Eindhoven, Department Of Mathematics And Computer Science","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"TU Eindhoven, Eindhoven Artificial Intelligence Systems Institute (EAISI)","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2023-09-19","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[".csv"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset accompanies the paper `Learning the mechanisms of network growth' by the same authors. The dataset contains 6733 networks of size 20,000 each generated in accordance to different combination of three mechanisms: fitness, aging and preferential attachment. The goal is to use machine learning to identify the combination of mechanisms that was used to create the network. The dataset includes static features from the literature and two version of our newly developed dynamic features. network 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