Explore projects

ATEM is 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. paper: https://arxiv.org/abs/2306.02221

RankMerging version in C++ as coded by Dan Timsit in 2024.

PCCMerge: a parallel method based on merging partial connected components in large graphs

Code accompanying the paper Strategic Candidacy Equilibria for Common Voting Rules. Jérôme Lang, Nicolas Maudet, Maria Polukarov, Alice Cohen-Hadria. Theory of Computing Systems. Vol. 69, Number 23. 2025

A decentralized learning research framework