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    François Bouchet / DaMiVis

    Creative Commons Attribution Non Commercial 4.0 International

    Sources du cours Data Mining & Visualisation (M1 Master Management de l'Innovation - Sorbonne Université)

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  • A

    equipebd / atem

    GNU General Public License v2.0 or later

    ATEM is a novel framework for studying topic evolution in scientific archives. It is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles.

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