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20072023
most citedMinimal Exploration in Structured Stochastic Bandits

79 citations · 519 across the 58 of their papers we have counts for

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9 papers · 1 filter

cs.SI2023

Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model

Kaito Ariu, Alexandre Proutiere, Se-Young Yun

In this paper, we investigate the problem of recovering hidden communities in the Labeled Stochastic Block Model (LSBM) with a finite number of clusters whose sizes grow linearly w…

cs.SI2015

Boolean Gossip Networks

Bo Li, Junfeng Wu, Hongsheng Qi +2

This paper proposes and investigates a Boolean gossip model as a simplified but non-trivial probabilistic Boolean network. With positive node interactions, in view of standard theo…

cs.SI2014★ 64 cited

Accurate Community Detection in the Stochastic Block Model via Spectral Algorithms

Se-Young Yun, Alexandre Proutiere

We consider the problem of community detection in the Stochastic Block Model with a finite number of communities of sizes linearly growing with the network size . This model…

cs.SI2014

Emergent Behaviors over Signed Random Dynamical Networks: Relative-State-Flipping Model

Guodong Shi, Alexandre Proutiere, Mikael Johansson +2

We study asymptotic dynamical patterns that emerge among a set of nodes interacting in a dynamically evolving signed random network, where positive links carry out standard consens…

cs.SI2014★ 12 cited

Streaming, Memory Limited Algorithms for Community Detection

Se-Young Yun, Marc Lelarge, Alexandre Proutiere

In this paper, we consider sparse networks consisting of a finite number of non-overlapping communities, i.e. disjoint clusters, so that there is higher density within clusters tha…

cs.SI2014★ 25 cited

Emergent Behaviors over Signed Random Dynamical Networks: State-Flipping Model

Guodong Shi, Alexandre Proutiere, Mikael Johansson +2

Recent studies from social, biological, and engineering network systems have drawn attention to the dynamics over signed networks, where each link is associated with a positive/neg…