Aleksandar Bojchevski | DeepAI

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∙ 03/07/2023

Probing Graph Representations

Today we have a good theoretical understanding of the representational p...

0 Mohammad Sadegh Akhondzadeh, et al. ∙

∙ 02/06/2023

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks

In tasks like node classification, image segmentation, and named-entity ...

0 Jan Schuchardt, et al. ∙

∙ 01/31/2023

Are Defenses for Graph Neural Networks Robust?

A cursory reading of the literature suggests that we have made a lot of ...

0 Felix Mujkanovic, et al. ∙

∙ 01/05/2023

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

Randomized smoothing is one of the most promising frameworks for certify...

0 Yan Scholten, et al. ∙

∙ 12/09/2022

Adversarial Weight Perturbation Improves Generalization in Graph Neural Network

A lot of theoretical and empirical evidence shows that the flatter local...

0 Yihan Wu, et al. ∙

∙ 10/28/2022

Localized Randomized Smoothing for Collective Robustness Certification

Models for image segmentation, node classification and many other tasks ...

0 Jan Schuchardt, et al. ∙

∙ 10/15/2022

Unveiling the Sampling Density in Non-Uniform Geometric Graphs

A powerful framework for studying graphs is to consider them as geometri...

0 Raffaele Paolino, et al. ∙

∙ 10/26/2021

Robustness of Graph Neural Networks at Scale

Graph Neural Networks (GNNs) are increasingly important given their popu...

0 Simon Geisler, et al. ∙

∙ 10/21/2021

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness

End-to-end (geometric) deep learning has seen first successes in approxi...

5 Simon Geisler, et al. ∙

∙ 08/29/2020

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More

Existing techniques for certifying the robustness of models for discrete...

0 Aleksandar Bojchevski, et al. ∙

∙ 07/03/2020

Scaling Graph Neural Networks with Approximate PageRank

Graph neural networks (GNNs) have emerged as a powerful approach for sol...

0 Aleksandar Bojchevski, et al. ∙

∙ 10/31/2019

Certifiable Robustness to Graph Perturbations

Despite the exploding interest in graph neural networks there has been l...

0 Aleksandar Bojchevski, et al. ∙

∙ 10/30/2019

Group Centrality Maximization for Large-scale Graphs

The study of vertex centrality measures is a key aspect of network analy...

0 Eugenio Angriman, et al. ∙

∙ 11/14/2018

Pitfalls of Graph Neural Network Evaluation

Semi-supervised node classification in graphs is a fundamental problem i...

0 Oleksandr Shchur, et al. ∙

∙ 10/14/2018

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank

Neural message passing algorithms for semi-supervised classification on ...

0 Johannes Klicpera, et al. ∙

∙ 06/03/2018

Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep l...

0 Federico Monti, et al. ∙

∙ 03/02/2018

NetGAN: Generating Graphs via Random Walks

We propose NetGAN - the first implicit generative model for graphs able ...

0 Aleksandar Bojchevski, et al. ∙

∙ 07/12/2017

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Methods that learn representations of graph nodes play a critical role i...

0 Aleksandar Bojchevski, et al. ∙

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