Abstract
Understanding the semantic behind relational data is very challenging, especially, when it is tricky to provide efficient analysis at scale. Furthermore, the complexity is also driven by the dynamical nature of data. Indeed, the analysis given at a specific time point becomes unsustainable even incorrect over time. In this paper, we rely on a visual interactive approach to handle Twitter’s networks using NLCOMS. NLCOMS provides multiple and coordinated views in order to grasp the underlying information. Finally, the applicability of the proposed approach is assessed on real-world data of the ANR-Info-RSN project.
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Acknowledgements
We would like to thank the anonymous referees for their pertinent remarks which improved the presentation of this paper. This research has been supported by the Agence Nationale de Recherche (ANR, France) during the Info-RSN Project.
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Abdelsadek, Y., Chelghoum, K., Herrmann, F., Kacem, I., Otjacques, B. (2016). Visual Interactive Approach for Mining Twitter’s Networks. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_34
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DOI: https://doi.org/10.1007/978-3-319-40973-3_34
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