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Article: Towards understanding longitudinal collaboration networks: a case of mammography performance research

TitleTowards understanding longitudinal collaboration networks: a case of mammography performance research
Authors
Issue Date2015
Citation
Scientometrics, 2015, v. 103 n. 2, p. 531-544 How to Cite?
AbstractIn this paper, we explore the longitudinal research collaboration network of ‘mammography performance’ over 30 years by creating and analysing a large collaboration network data using Scopus. The study of social networks using longitudinal data may provide new insights into how this collaborative research evolve over time as well as what type of actors influence the whole network in time. The methods and findings presented in this work aim to assist identifying key actors in other research collaboration networks. In doing so, we apply a rank aggregation technique to centrality measures in order to derive a single ranking of influential actors. We argue that there is a strong correlation between the level of degree and closeness centralities of an actor and its influence in the research collaboration network (at macro/country level).
Persistent Identifierhttp://hdl.handle.net/10722/209828

 

DC FieldValueLanguage
dc.contributor.authorHossain, Len_US
dc.date.accessioned2015-05-18T03:27:03Z-
dc.date.available2015-05-18T03:27:03Z-
dc.date.issued2015en_US
dc.identifier.citationScientometrics, 2015, v. 103 n. 2, p. 531-544en_US
dc.identifier.urihttp://hdl.handle.net/10722/209828-
dc.description.abstractIn this paper, we explore the longitudinal research collaboration network of ‘mammography performance’ over 30 years by creating and analysing a large collaboration network data using Scopus. The study of social networks using longitudinal data may provide new insights into how this collaborative research evolve over time as well as what type of actors influence the whole network in time. The methods and findings presented in this work aim to assist identifying key actors in other research collaboration networks. In doing so, we apply a rank aggregation technique to centrality measures in order to derive a single ranking of influential actors. We argue that there is a strong correlation between the level of degree and closeness centralities of an actor and its influence in the research collaboration network (at macro/country level).en_US
dc.languageengen_US
dc.relation.ispartofScientometricsen_US
dc.titleTowards understanding longitudinal collaboration networks: a case of mammography performance researchen_US
dc.typeArticleen_US
dc.identifier.emailHossain, L: lhossain@hku.hken_US
dc.identifier.authorityHossain, L=rp01858en_US
dc.identifier.doi10.1007/s11192-015-1560-3en_US
dc.identifier.hkuros243372en_US
dc.identifier.volume103en_US
dc.identifier.issue2-
dc.identifier.spage531en_US
dc.identifier.epage544en_US

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