Search Results for author: Johannes F. Lutzeyer

Found 8 papers, 7 papers with code

A Simple and Yet Fairly Effective Defense for Graph Neural Networks

1 code implementation21 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.

Adversarial Defense Node Classification

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

1 code implementation8 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.

Graph Ordering Attention Networks

1 code implementation11 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.

Node Classification

Node Feature Kernels Increase Graph Convolutional Network Robustness

1 code implementation4 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.

Node Classification

Sparsifying the Update Step in Graph Neural Networks

1 code implementation2 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.

Analysing the Update step in Graph Neural Networks via Sparsification

no code implementations1 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.

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