Recommending intra-institutional scientific collaboration through coauthorship network visualization

dc.contributorInstituto Tecnológico y de Estudios Superiores de Monterrey
dc.creatorPrada, Gustavo A
dc.creatorCantu-Ortiz, Francisco J
dc.creatorRodríguez-Acevez, Lucía Alejandra
dc.creatorCeballos Cansino, Héctor Gibrán
dc.date.accessioned2020-03-14T00:27:53Z
dc.date.accessioned2022-10-13T21:54:14Z
dc.date.available2020-03-14T00:27:53Z
dc.date.available2022-10-13T21:54:14Z
dc.date.created2020-03-14T00:27:53Z
dc.date.issued2013-10-28
dc.identifier978-145032414-4
dc.identifier10.1145/2508497.2508499
dc.identifierhttp://hdl.handle.net/11285/636187
dc.identifierCompSci 2013 - Proceedings of the 2013 Workshop on Computational Scientometrics: Theory and Applications, Co-located with CIKM 2013
dc.identifier7
dc.identifier12
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4223341
dc.description.abstractFor improving research productivity, quality and dissemination, we propose the development of a visual recommendation tool summing up scientific collaboration best-practices found in literature. Social Network Analysis are applied to a coauthorship network for generating a Potential Collaboration Index (PCI) based on productivity, connectivity, similarity and expertise. This work is evaluated by recommending intra-institutional collaboration in a comprehensive university. The accuracy of PCI is documented, along with suggestions and comments from 27 interviewed researchers.
dc.languageeng
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsEmbargoed Access
dc.titleRecommending intra-institutional scientific collaboration through coauthorship network visualization
dc.typeArtículo de Conferencia / Conference Article


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