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This paper presents ATEM, a novel framework for studying topic evolution inscientific archives. ATEM is based on dynamic topic modeling and dynamic graphembedding techniques that explore the dynamics of content and citations ofdocuments within a scientific corpus. ATEM explores a new notion of contextualemergence for the discovery of emerging interdisciplinary research topics basedon the dynamics of citation links in topic clusters. Our experiments show thatATEM can efficiently detect emerging cross-disciplinary topics within the DBLParchive of over five million computer science articles.

ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives