Unpublished conference/Abstract (Scientific congresses, symposiums and conference proceedings)
Transforming Collaboration Data into Network Layers for Enhanced Analytics
Esmaeilzadeh Dilmaghani, Saharnaz; Piyatumrong, Apivadee; Bouvry, Pascal et al.
2019International Conference on Optimization and Learning
 

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Keywords :
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.
Disciplines :
Computer science
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)
External co-authors :
yes
Language :
English
Title :
Transforming Collaboration Data into Network Layers for Enhanced Analytics
Publication date :
25 February 2019
Event name :
International Conference on Optimization and Learning
Event date :
from 29-01-2019 to 31-01-2019
Audience :
International
Focus Area :
Computational Sciences
Available on ORBilu :
since 01 April 2019

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