Fiche de FETTAL Chakib

Titre : Contributions to Scalable Clustering of Networks and Graphs
Date : 2 février 2024
Heure: 10:00
Résumé : This thesis tries to address scalability issues of the state-of-the-art graph clustering models and presents approaches for clustering and representation learning different types of graphs, including classical graphs, bipartite graphs, attributed graphs, bipartite attributed graphs, and multi-view attributed graphs. To this end we leverage techniques such as : linear projections, Laplacian smoothing, optimal transport, etc. The proposed approaches all share three key characteristics: simplicity, cost-effectiveness, and having few hyper-parameters. Thanks to their simple yet effective nature, the proposed methods are competitive with the state of the art while also generally being more computationally efficient. We showcase the efficacy and efficiency of our models against state-of-the-art methods through extensive experimentation and significance testing.
Lieu : 45, Rue des Saints-Pères, 75006 Paris, France