11 citations · 19 across the 4 of their papers we have counts for
4 papers
Leiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings
Yuhe Bai, Camelia Constantin, Hubert Naacke
In the area of large-scale training of graph embeddings, effective training frameworks and partitioning methods are critical for handling large networks. However, they face two maj…
ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives
Hamed Rahimi, Hubert Naacke, Camelia Constantin +1
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…
Contextualized Topic Coherence Metrics
Hamed Rahimi, Jacob Louis Hoover, David Mimno +3
The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification…
ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics
Hamed Rahimi, Hubert Naacke, Camelia Constantin +1
This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framewo…