ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives
This work addresses the challenge of identifying new cross-disciplinary research trends for researchers and archivists, representing an incremental advancement in topic evolution modeling.
The paper tackles the problem of detecting emerging interdisciplinary research topics in scientific archives by introducing ATEM, a framework that uses dynamic topic modeling and graph embedding to analyze content and citation dynamics, achieving efficient detection in a corpus of over five million articles.
This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM 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.