4 papers
When Does Bottom-up Beat Top-down in Hierarchical Community Detection?
Maximilien Dreveton, Daichi Kuroda, Matthias Grossglauser +1
Hierarchical clustering of networks consists in finding a tree of communities, such that lower levels of the hierarchy reveal finer-grained community structures. There are two main…
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics
Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda +1
Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods,…
Optimal Graph Clustering without Edge Density Signals
Maximilien Dreveton, Elaine Siyu Liu, Matthias Grossglauser +1
This paper establishes the theoretical limits of graph clustering under the Popularity-Adjusted Block Model (PABM), addressing limitations of existing models. In contrast to the St…
Why the Metric Backbone Preserves Community Structure
Maximilien Dreveton, Charbel Chucri, Matthias Grossglauser +1
The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges that are not the shortest path between and . I…