untitled
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<responseDate>2018-01-17T12:18:48Z</responseDate>
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<identifier>oai:HAL:hal-01637563v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Link Clustering for Extracting Collaborative Patterns in a Scientific Co-Authored Network</title>
<creator>Stattner, Erick</creator>
<creator>Collard, Martine</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>EEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source>
<source>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source>
<coverage>Sydney, Australia</coverage>
<contributor>IEEE</contributor>
<contributor>ACM</contributor>
<identifier>hal-01637563</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01637563</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01637563</source>
<source>IEEE; ACM. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017, Sydney, Australia. EEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp.1002-1008 2017, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 〈10.1145/3110025.3110146〉</source>
<identifier>DOI : 10.1145/3110025.3110146</identifier>
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<language>en</language>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this article, we analyse a collaborative network to understand the underlying patterns that structure the co-writing process of scientific articles. Our goal is to identify and understand the collaboration tendencies from authors publishing activities. For this purpose, we adopt a descriptive modelling through a network approach that consists first in generating the collaborative network from data on publications. Nodes of the network are then enriched with a set of individual attributes extracted from the publishing activity of each author. Finally, we search for conceptual views, a recent link clustering approach, which allows to summarize any kind of networks by highlighting the sets of attributes found frequently linked. Results show that it exists strong tendencies that unconsciously structure the collaboration behaviours. In this paper, we present these tendencies and show how they evolve according to different extraction thresholds.</description>
<date>2017</date>
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