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Spectral MAB for Unknown Graph Processes

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Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the ...

EPFL2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

Pascal Frossard, Effrosyni Simou

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Partition and Code: learning how to compress graphs

Michael Bronstein, Andreas Loukas, Nikolaos Karalias, Georgios Bouritsas

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

2021

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

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On The Choice of Graph Neural Network Architectures

Pascal Frossard, Guillermo Ortiz Jimenez, Clément Arthur Yvon Vignac

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

2020

Efficient large-scale graph processing: optimisations for storage, performance and evolving graphs

Jasmina Malicevic

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks. Algorithms to mine graphs incur many random accesses, and the sparse nature of the graphs of interest, exacerbates this. As DRAM sustains high bandwidt ...

EPFL2019

Slepian Guided Filtering of Graph Signals

Dimitri Nestor Alice Van De Ville, Miljan Petrovic

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Graph Reduction with Spectral and Cut Guarantees

Andreas Loukas

Can one reduce the size of a graph without significantly altering its basic properties? The graph reduction problem is hereby approached from the perspective of restricted spectral approximation, a modification of the spectral similarity measure used for g ...

MICROTOME PUBL2019

Graph-based image representation learning

Renata Khasanova

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

EPFL2019