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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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Python code for online version of pks generation algorithm
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EPIQUE: Exploring the Evolution of Science with Pivot Topic Graphs
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Alliance/Coriolis Toolkit & Checker contain various design flow examples, blocks or full chips. It is also used as a regression test for all the tools.
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Code python pour les expériences de génération de graphes aléatoires
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