
Interpretable Stability Bounds for Spectral Graph Filters
Graphstructured data arise in a variety of realworld context ranging f...
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On the Stability of Graph Convolutional Neural Networks under Edge Rewiring
Graph neural networks are experiencing a surge of popularity within the ...
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Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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node2coords: Graph Representation Learning with Wasserstein Barycenters
In order to perform network analysis tasks, representations that capture...
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Mask Combination of Multilayer Graphs for Global Structure Inference
Structure inference is an important task for network data processing and...
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Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder
While the prevalence of Autism Spectrum Disorder (ASD) is increasing, re...
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Learning Graphs from Data: A Signal Representation Perspective
The construction of a meaningful graph topology plays a crucial role in ...
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Graphbased Transform Coding with Application to Image Compression
In this paper, we propose a new graphbased coding framework and illustr...
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Graph learning under sparsity priors
Graph signals offer a very generic and natural representation for data t...
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Learning heat diffusion graphs
Effective information analysis generally boils down to properly identify...
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Graphbased compression of dynamic 3D point cloud sequences
This paper addresses the problem of compression of 3D point cloud sequen...
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Learning Laplacian Matrix in Smooth Graph Signal Representations
The construction of a meaningful graph plays a crucial role in the succe...
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Learning parametric dictionaries for graph signals
In sparse signal representation, the choice of a dictionary often involv...
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Dorina Thanou
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