Reference : Transforming Collaboration Data into Network Layers for Enhanced Analytics
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/39241
Transforming Collaboration Data into Network Layers for Enhanced Analytics
English
Esmaeilzadeh Dilmaghani, Saharnaz mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Piyatumrong, Apivadee mailto [National Electronics and Computer Technology Center (NECTEC), Thailand]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Brust, Matthias R. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
25-Feb-2019
International Conference on Optimization and Learning
Yes
International
[en] data analysis ; Collaboration data ; interaction methods
[en] We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental patterns and insights of the research collaborations. In this paper, we describe our data-to-networks method, which automatically generates a set of logical network layers from the relational input data using a linkage threshold. We, then, use a series of network metrics to analyze the impact of the linkage threshold on the individual network layers. Moreover, results from the network analysis also provide beneficial information to improve the network visualization. We demonstrate the feasibility and impact of our approach using real-world collaboration data. We discuss how the produced network layers can reveal insights and patterns to direct the data analytics more intelligently.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/39241

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