Transforming Collaboration Data into Network Layers for Enhanced Analytics

Esmaeilzadeh Dilmaghani, Saharnaz; Piyatumrong, Apivadee; Bouvry, Pascal; Brust, Matthias R.

Reference : Transforming Collaboration Data into Network Layers for Enhanced Analytics


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/39241

Title :	Transforming Collaboration Data into Network Layers for Enhanced Analytics
Language :	English
Author, co-author :	Esmaeilzadeh Dilmaghani, Saharnaz [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Piyatumrong, Apivadee [National Electronics and Computer Technology Center (NECTEC), Thailand]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Brust, Matthias R. [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	25-Feb-2019
Peer reviewed :	Yes
Audience :	International
Event name :	International Conference on Optimization and Learning
Event date :	from 29-01-2019 to 31-01-2019
Keywords :	[en] Data analysis ; Collaboration data ; Interaction methods
Abstract :	[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.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/39241

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