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20172021
most citedInterpretable Stability Bounds for Spectral Graph Filters

4 citations · 4 across the 1 of their papers we have counts for

collaborators

8 papers

cs.LG20214 cited

Interpretable Stability Bounds for Spectral Graph Filters

Henry Kenlay, Dorina Thanou, Xiaowen Dong

Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-struc…

cs.LG2020

On the Stability of Graph Convolutional Neural Networks under Edge Rewiring

Henry Kenlay, Dorina Thanou, Xiaowen Dong

Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to non-Euclidean domains and instil inductive biase…

cs.LG2020

Graph signal processing for machine learning: A review and new perspectives

Xiaowen Dong, Dorina Thanou, Laura Toni +2

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are o…

cs.LG2020

node2coords: Graph Representation Learning with Wasserstein Barycenters

Effrosyni Simou, Dorina Thanou, Pascal Frossard

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn rep…

cs.LG2019

Mask Combination of Multi-layer Graphs for Global Structure Inference

Eda Bayram, Dorina Thanou, Elif Vural +1

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph st…

eess.IV2019

Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder

Sarah Itani, Dorina Thanou

While the prevalence of Autism Spectrum Disorder (ASD) is increasing, research continues in an effort to identify common etiological and pathophysiological bases. In this regard, m…