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

Seyedamir Tavakoli Taba, Liaquat Hossain, Simon Reay Atkinson, Sarah Lewis

Research output: Contribution to journalArticle

Abstract

In 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).

Original languageEnglish
Pages (from-to)531-544
Number of pages14
JournalScientometrics
Volume103
Issue number2
Early online date15 Mar 2015
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Fingerprint Dive into the research topics of 'Towards understanding longitudinal collaboration networks: a case of mammography performance research'. Together they form a unique fingerprint.

Cite this