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Publications associées (27)

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This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

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

Percolation and phase behavior in cellulose nanocrystal suspensions from nonlinear rheological analysis

Tiffany Abitbol

We examine the influence of surface charge on the percolation, gel-point and phase behavior of cellulose nanocrystal (CNC) suspensions in relation to their nonlinear rheological material response. Desulfation decreases CNC surface charge density which lead ...

ELSEVIER SCI LTD2023

The Role of Adaptivity in Source Identification with Time Queries

Gergely Odor

Understanding epidemic propagation in large networks is an important but challenging task, especially since we usually lack information, and the information that we have is often counter-intuitive. An illustrative example is the dependence of the final siz ...

EPFL2022

Learning Bollobas-Riordan Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...

IEEE2021

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 ...

2021

Local Tomography of Large Networks Under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Graph Learning with Partial Observations: Role of Degree Concentration

Ali H. Sayed, Augusto José Rabelo Almeida Santos

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

IEEE2019

Consistent Tomography over Diffusion Networks under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete dataset ...

IEEE2018

Assembling a Network out of Ambiguous Patches

Matthias Grossglauser, Lyudmila Yartseva, Jefferson Elbert Simões

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small {\em patches} or subgraphs, of the true net ...

2016