Generalized Blockmodeling of Multi-Valued Networks

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dc.contributor.authorBrown, Nathanael
dc.contributor.authorNozick, Linda
dc.date.accessioned2020-01-04T07:39:46Z
dc.date.available2020-01-04T07:39:46Z
dc.date.issued2020-01-07
dc.description.abstractThis research presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity blockmodeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null (all zeros), complete (all ones) and valued. Two case studies are presented: the Southern Women dataset and a larger example using a subset of the IMDb movie dataset.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.302
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64044
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectblockmodeling
dc.subjectgeneralized blockmodeling
dc.subjectvalued network
dc.subjectclustering
dc.titleGeneralized Blockmodeling of Multi-Valued Networks
dc.typeConference Paper
dc.type.dcmiText

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