Book

1. Statistical Analysis of Networks
   
   Konstantin Avrachenkov and Maximilien Dreveton, 2022
2. Leçons pour l’agrégation de mathématiques-Préparation à l’oral
   
   Maximilien Dreveton and Joachim Lhabouz
   Editions Ellipses, 2019

Preprint

1. Estimation and Recovery of a Planted Dense Subgraph from a Single Network Cascade
   Maximilien Dreveton, Paula Mürmann, Patrick Thiran
   arXiv preprint arXiv:2610.08766
2. Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance 
   Daichi Kuroda, Maximilien Dreveton, Matthias Grossglauser, Patrick Thiran 
   arXiv preprint arXiv:2609.11173, 2026
3. Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics 
   Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda,  Patrick Thiran 
   arXiv preprint arXiv:2511.18056, 2025
4. Recovering Small Communities in the Planted Partition Model
   
   Martijn Gösgens, and Maximilien Dreveton
   arXiv preprint arXiv:2504.01663, 2025
5. Community recovery in non-binary and temporal stochastic block models
   
   Konstantin Avrachenkov, Maximilien Dreveton, and Lasse Leskelä
   arXiv preprint arXiv:2008.04790, 2020

Journal

1. Robust estimation of a Markov chain transition matrix from multiple sample paths
   Lasse Leskelä and Maximilien Dreveton
   Statistica Neerlandica, 2026
2. When Does Bottom-up Beat Top-down in Hierarchical Community Detection?
   
   Maximilien Dreveton, Daichi Kuroda, Matthias Grossglauser, and Patrick Thiran
   Journal of the American Statistical Association, 2025
3. Almost exact recovery in noisy semi-supervised learning
   
   Konstantin Avrachenkov, and Maximilien Dreveton
   Probability in the Engineering and Informational Sciences, 2025
4. Recovering static and time-varying communities using persistent edges
   
   Konstantin Avrachenkov, Maximilien Dreveton, and Lasse Leskelä
   IEEE Transactions On Network Science And Engineering, 2023
5. Higher-order spectral clustering for geometric graphs
   
   Konstantin Avrachenkov, Andrei Bobu, and Maximilien Dreveton
   Journal of Fourier Analysis and Applications, 2021

Conference

1. Optimal Graph Clustering without Edge Density Signals
   Maximilien Dreveton, Elaine Siyu Liu, Matthias Grossglauser, and Patrick Thiran
   In The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
2. A Framework for Efficient Estimation of Closeness Centrality and Eccentricity in Large Networks
   Patrick C. Trindade, Maximilien Dreveton, and Daniel R. Figueiredo
   In International Conference on Complex Networks, 2025
3. Reducing Sensor Requirements by Relaxing the Network Metric Dimension
   Paula Mürmann, Robin Jaccard, Maximilien Dreveton, Aryan Alavi Razavi Ravari, and Patrick Thiran
   In Proceedings of the ACM on Measurement and Analysis of Computer Systems (SIGMETRICS), 2025
4. Why the Metric Backbone Preserves Community Structure
   Maximilien Dreveton, Charbel Chucri, Matthias Grossglauser, and Patrick Thiran
   In The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
5. Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
   Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, and Patrick Thiran
   In Proceedings of Thirty Seventh Conference on Learning Theory (COLT), 2024
6. Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model
   Maximilien Dreveton, Felipe Fernandes, and Daniel Figueiredo
   In The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
7. Almost exact recovery in label spreading
   Konstantin Avrachenkov and Maximilien Dreveton
   In Algorithms and Models for the Web Graph: 16th International Workshop, WAW 2019, Brisbane, QLD, Australia, July 6–7, 2019, Proceedings 16, 2019