Publications by Maximilien Claude Robert Dreveton

Related publications (6)

Reducing Sensor Requirements by Relaxing the Network Metric Dimension

Robin Jaccard, Paula Anette Mürmann, Patrick Thiran, Maximilien Claude Robert Dreveton

Source localization in graphs involves identifying the origin of a phenomenon or event, such as an epidemic outbreak or a misinformation source, by leveraging structural graph properties. One key concept in this context is the metric dimension, which quant ...

Association for Computing Machinery (ACM)2025

Why the Metric Backbone Preserves Community Structure

Charbel Chucri, Patrick Thiran, Matthias Grossglauser, Maximilien Claude Robert Dreveton

The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges

(u,v)

that are not the shortest path between

u

and

v

. In networks with well-separated communities, the metric backbone tends to pre ...

2024

Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model

Maximilien Claude Robert Dreveton

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (ed ...

2023

Almost exact recovery in noisy semi-supervised learning

Maximilien Claude Robert Dreveton

2024.Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classification. In particular, we derive the maximum a posteriori (MAP) estima ...

2024

Recovering Static and Time-Varying Communities Using Persistent Edges

Maximilien Claude Robert Dreveton

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

Ieee Computer Soc2024

Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models

Patrick Thiran, Matthias Grossglauser, Maximilien Claude Robert Dreveton

Clustering is a pivotal challenge in unsupervised machine learning and is often investigated through the lens of mixture models. The optimal error rate for recovering cluster labels in Gaussian and sub-Gaussian mixture models involves ad hoc signal-to-nois ...

2024