Johannes F. Lutzeyer

CoRR, 2024

A Simple and Yet Fairly Effective Defense for Graph Neural Networks.

[BibT_eX]

[DOI]

Sofiane Ennadir

Yassine Abbahaddou

Henrik Boström

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Path Neural Networks: Expressive and Accurate Graph Neural Networks.

[BibT_eX]

[DOI]

Gaspard Michel

Giannis Nikolentzos

Proceedings of the International Conference on Machine Learning, 2023

How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Graph Ordering Attention Networks.

[BibT_eX]

[DOI]

Michail Chatzianastasis

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Modularity-aware graph autoencoders for joint community detection and link prediction.

[BibT_eX]

[DOI]

Guillaume Salha-Galvan

Romain Hennequin

Neural Networks, 2022

New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction.

[BibT_eX]

[DOI]

Guillaume Salha-Galvan

Romain Hennequin

CoRR, 2022

Improving Graph Neural Networks at Scale: Combining Approximate PageRank and CoreRank.

[BibT_eX]

[DOI]

Ariel R. Ramos Vela

Anastasios Giovanidis

CoRR, 2022

Sparsifying the Update Step in Graph Neural Networks.

[BibT_eX]

[DOI]

Changmin Wu

Proceedings of the Topological, 2022

Node Feature Kernels Increase Graph Convolutional Network Robustness.

[BibT_eX]

[DOI]

Mohamed El Amine Seddik

Changmin Wu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Learning Parametrised Graph Shift Operators.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2019

Comparing Spectra of Graph Shift Operator Matrices.

[BibT_eX]

[DOI]