Publications associées à Non-Exponential Variations for Classical Results in First Passage Percolation

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.

We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Elsevier2024

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Ieee Computer Soc2024

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Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...

EPFL2023

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This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

2023

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We study the problem of identifying the source of a stochastic diffusion process spreading on a graph based on the arrival times of the diffusion at a few queried nodes. In a graph

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In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the k ...

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Sequential metric dimension for random graphs

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In the localization game on a graph, the goal is to find a fixed but unknown target node v* with the least number of distance queries possible. In the j-th step of the game, the player queries a single node v_j and receives, as an answer to their query, th ...

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Faster Monotone Min-Plus Product, Range Mode, and Single Source Replacement Paths

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One of the most basic graph problems, All-Pairs Shortest Paths (APSP) is known to be solvable in n^{3-o(1)} time, and it is widely open whether it has an O(n^{3-ε}) time algorithm for ε > 0. To better understand APSP, one often strives to obtain subcubic t ...

Schloss Dagstuhl -- Leibniz-Zentrum für Informatik2021

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Non-fungible tokens (NFTs) as a decentralized proof of ownership represent one of the main reasons why Ethereum is a disruptive technology. This paper presents the first systematic study of the interactions occurring in a number of NFT ecosystems. We illus ...

IEEE2021

On the asymptotic behavior of solutions to the Vlasov-Poisson system

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We prove small data modified scattering for the Vlasov-Poisson system in dimension

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2021

Non-Exponential Variations for Classical Results in First Passage Percolation

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The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.

Recovering Static and Time-Varying Communities Using Persistent Edges

Equivariant Neural Architectures for Representing and Generating Graphs

Maximum Independent Set: Self-Training through Dynamic Programming

The power of adaptivity in source identification with time queries on the path

Figlearn: Filter And Graph Learning Using Optimal Transport

Sequential metric dimension for random graphs

Faster Monotone Min-Plus Product, Range Mode, and Single Source Replacement Paths

Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem

On the asymptotic behavior of solutions to the Vlasov-Poisson system

Recovering Static and Time-Varying Communities Using Persistent Edges

Equivariant Neural Architectures for Representing and Generating Graphs

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al.

On the asymptotic behavior of solutions to the Vlasov-Poisson system

Faster Monotone Min-Plus Product, Range Mode, and Single Source Replacement Paths

The power of adaptivity in source identification with time queries on the path

Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem

Sequential metric dimension for random graphs

Maximum Independent Set: Self-Training through Dynamic Programming

Figlearn: Filter And Graph Learning Using Optimal Transport