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

Hamed Rahimi (1) , Hubert Naacke (1) , Camelia Constantin (1) , Bernd Amann (1)

1 BD - Bases de Données

This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM employs dynamic topic modeling and dynamic graph embedding to explore the dynamics of content and citations within a scientific corpus. ATEM explores a new notion of citation context that uncovers emerging topics by analyzing the dynamics of citation links between evolving topics. Our experiments demonstrate that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles.

Mots clés: Evolution Model; Topic Emergence; Science Evolution

Domaines: Informatique [cs]; Base de données [cs.DB]

Hamed Rahimi, Hubert Naacke, Camelia Constantin, Bernd Amann. ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives. COMPLEX NETWORKS 2023 - The 12th International Conference on Complex Networks and their Applications, Nov 2023, Menton, France. pp.332-343, ⟨10.1007/978-3-031-53472-0_28⟩. ⟨hal-04495320⟩