ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives
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Abstract (arXiv)
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.
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References · 11
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
- 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
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- 2020Top2Vec: Distributed Representations of Topicscited by 124
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- 2018dynnode2vec: Scalable Dynamic Network Embeddingcited by 32
- 2023ANTM: An Aligned Neural Topic Model for Exploring Evolving Topicscited by 6
- 2015Discovering topic structures of a temporally evolving document corpuscited by 3
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