The massive explosion of data collection led to a multi-disciplinary interest in the statistical inference of complex systems. In these systems, agents interact by pairs. Since similar agents tend to interact similarly, an important unsupervised learning problem consists of grouping the agents into communities or clusters based on the pairwise...
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April 6, 2022 (v1)PublicationUploaded on: December 3, 2022
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October 6, 2022 (v1)Book
This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The...
Uploaded on: February 22, 2023 -
May 28, 2019 (v1)Book
Ce livre sur l'oral de l'agrégation externe de mathématiques comporte des plans complets de 76 leçons d'algèbre et d'analyse. Sont principalement concernés les candidats à l'agrégation externe, mais ceux du concours interne ou du Capes pourront aussi y trouver des passages utiles.Les plans sont rédigés avec la rigueur attendue par le jury, et...
Uploaded on: December 4, 2022 -
July 6, 2019 (v1)Conference paper
In semi-supervised graph clustering setting, an expert provides cluster membership of few nodes. This little amount of information allows one to achieve high accuracy clustering using efficient computational procedures. Our main goal is to provide a theoretical justification why the graph-based semi-supervised learning works very well....
Uploaded on: December 4, 2022 -
March 15, 2021 (v1)Journal article
The present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering. It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector...
Uploaded on: December 4, 2022 -
November 15, 2021 (v1)Conference paper
This article studies the recovery of static communities in a temporal network. We introduce a temporal stochastic block model where dynamic interaction patterns between node pairs follow a Markov chain. We render this model versatile by adding degree correction parameters, describing the tendency of each node to start new interactions. We show...
Uploaded on: December 3, 2022