Concept

Cayley graph

Related publications (81)

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fGOT: Graph Distances Based on Filters and Optimal Transport

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic, Giovanni Chierchia

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Space-Efficient Representations of Graphs

Jakab Tardos

With the increasing prevalence of massive datasets, it becomes important to design algorithmic techniques for dealing with scenarios where the input to be processed does not fit in the memory of a single machine. Many highly successful approaches have emer ...

EPFL2022

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks

Pascal Frossard, Chenglin Li, Xing Gao

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspec ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Representing graphs through data with learning and optimal transport

Hermina Petric Maretic

Graphs offer a simple yet meaningful representation of relationships between data. Thisrepresentation is often used in machine learning algorithms in order to incorporate structuralor geometric information about data. However, it can also be used in an inv ...

EPFL2021

FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows

Effrosyni Simou

In several machine learning tasks for graph structured data, the graphs under consideration may be composed of a varying number of nodes. Therefore, it is necessary to design pooling methods that aggregate the graph representations of varying size to repre ...

2021

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

On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...

2020

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Stefano Zamuner, Margherita Marchi

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of ...

NATURE RESEARCH2020

Asymptotics of Cheeger constants and unitarisability of groups

Nicolas Monod, Maria Gerasimova

Given a group Gamma, we establish a connection between the unitarisability of its uniformly bounded representations and the asymptotic behaviour of the isoperimetric constants of Cayley graphs of Gamma for increasingly large generating sets. The connection ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Experiments with the Markoff Surface

Matthew De Courcy-Ireland, Sandy Lee

We confirm, for the primes up to 3000, the conjecture of Bourgain-Gamburd-Sarnak and Baragar on strong approximation for the Markoff surface modulo primes. For primes congruent to 3 modulo 4, we find data suggesting that some natural graphs constructed fro ...

TAYLOR & FRANCIS INC2020