1 code implementation • 21 Feb 2024 • Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström
Successful combinations of our NoisyGNN approach with existing defense techniques demonstrate even further improved adversarial defense results.
1 code implementation • 16 Nov 2022 • Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas, Romain Hennequin, Michalis Vazirgiannis
It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features.
1 code implementation • 8 Nov 2022 • Ariel R. Ramos Vela, Johannes F. Lutzeyer, Anastasios Giovanidis, Michalis Vazirgiannis
Graph Neural Networks (GNNs) have achieved great successes in many learning tasks performed on graph structures.
1 code implementation • 11 Apr 2022 • Michail Chatzianastasis, Johannes F. Lutzeyer, George Dasoulas, Michalis Vazirgiannis
The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node.
1 code implementation • 2 Feb 2022 • Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas, Romain Hennequin, Michalis Vazirgiannis
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction.
1 code implementation • 4 Sep 2021 • Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis
The robustness of the much-used Graph Convolutional Networks (GCNs) to perturbations of their input is becoming a topic of increasing importance.
1 code implementation • 2 Sep 2021 • Johannes F. Lutzeyer, Changmin Wu, Michalis Vazirgiannis
In this paper we conduct a structured, empirical study of the effect of sparsification on the trainable part of MPNNs known as the Update step.
no code implementations • 1 Jan 2021 • Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis
In recent years, Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural Network (GNN) framework, have celebrated much success in the analysis of graph-structured data.