Link Clustering for Extracting Collaborative Patterns in a Scientific Co-Authored Network

Abstract : 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.
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Communication dans un congrès
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〉
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https://hal.archives-ouvertes.fr/hal-01637563
Contributeur : Erick Stattner <>
Soumis le : vendredi 17 novembre 2017 - 16:09:42
Dernière modification le : lundi 26 février 2018 - 21:24:51

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Erick Stattner, Martine Collard. Link Clustering for Extracting Collaborative Patterns in a Scientific Co-Authored Network. 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〉. 〈hal-01637563〉

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