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Title: ACE : big data approach to scientific collaboration patterns analysis
Authors: Zammit, Andrei
Penza, Kenneth
Haddod, Foaad
Abela, Charlie
Azzopardi, Joel
Keywords: Big data -- Data processing
Big data -- Computer programs
College teachers as authors -- Bibliography -- Data processing
Exchange of bibliographic information -- Data processing
Bibliography -- Data processing
Data sets
Issue Date: 2017
Publisher: CEUR
Citation: Zammit, A., Penza, K., Haddod, F., Abela, C., & Azzopardi, J. (2017). ACE : big data approach to scientific collaboration patterns analysis. 1st Scientometrics Workshop, co-located with the 14th Extended Semantic Web Conference (ESWC), Portoro┼ż, 1-16.
Abstract: The characteristics of scientific collaboration networks have been extensively analysed and found to be similar to other scale-free networks. Research has furthermore focused on investigating how collaboration patterns between authors evolved over time, by providing insights into different fields of research. Numerous bibliographic datasets, such as DBLP and Microsoft Academic Graph, provide the basis for investigations and analysis of such networks. This paper presents ACE (Academic Collaboration analyzEr); an interactive framework that uses big data technologies and allows for scientific collaboration patterns to be analysed and visualised. Through ACE it is possible to reveal the key authors in particular fields of research, the topological features of the collaboration network, the network trends over time and the relationships between authors and co-authors. Furthermore, ACE allows for the discovery of potentially new collaborations between authors in the same field of research as well as fields where scientists can conduct future joint-research work.
ISSN: 16130073
Appears in Collections:Scholarly Works - FacICTAI

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