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Related publications (31)

Methods for functional connectivity and morphometry in neonatal neuroimaging to study neurodevelopment

Serafeim Loukas

Preterm birth is a major pediatric health problem that perturbs the genetically determined program of corticogenesis of the developing brain. As a consequence, prematurity has been strongly associated with adverse long-term neurodevelopmental outcome that ...

EPFL2021

Multiscale Representation Learning of Graph Data With Node Affinity

Pascal Frossard, Chenglin Li, Xing Gao

Graph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

The completeness of the generalized eigenfunctions and an upper bound for the counting function of the transmission eigenvalue problem for Maxwell equations

Hoài-Minh Nguyên, Jean Louis-Alexandre Fornerod

Cakoni and Nguyen recently proposed very general conditions on the coefficients of Maxwell equations for which they established the discreten ess of the set of eigenvalues of the transmission problem and studied their locations. In this paper, we establish ...

2021

Local approximation of the Maximum Cut in regular graphs

Etienne Michel François Bamas

This paper is devoted to the distributed complexity of finding an approximation of the maximum cut (MAXCUT) in graphs. A classical algorithm consists in letting each vertex choose its side of the cut uniformly at random. This does not require any communica ...

ELSEVIER2020

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Raphaël Pierre Liégeois, Miljan Petrovic

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

2020

Graph Coarsening with Preserved Spectral Properties

Andreas Loukas

In graph coarsening, one aims to produce a coarse graph of reduced size while preserving important graph properties. However, as there is no consensus on which specific graph properties should be preserved by coarse graphs, measuring the differences betwee ...

ADDISON-WESLEY PUBL CO2020

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

How hard is to distinguish graphs with graph neural networks?

Andreas Loukas

A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs. This study derives hardness results for the classification variant of graph isomorphism in the message-passing model (MPNN). MPNN encompasses the maj ...

2020

OrthoNet: Multilayer Network Data Clustering

Pascal Frossard, Mireille El Gheche, Giovanni Chierchia

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement

James Henderson, Alireza Mohammadshahi

We propose the Recursive Non-autoregressive Graph-to-graph Transformer architecture (RNG-Tr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dep ...

2021

Nested oscillations and brain connectivity during sequential stages of feature-based attention

David Pascucci, Gijs Plomp

Brain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based atten ...

2020

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