4 citations · 22 across the 28 of their papers we have counts for
3 papers · 1 filter
Node Feature Kernels Increase Graph Convolutional Network Robustness
Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer +1
The robustness of the much-used Graph Convolutional Networks (GCNs) to perturbations of their input is becoming a topic of increasing importance. In this paper, the random GCN is i…
Sparsifying the Update Step in Graph Neural Networks
Johannes F. Lutzeyer, Changmin Wu, Michalis Vazirgiannis
Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural Network (GNN) framework, celebrate much success in the analysis of graph-structured data. Concurrently, the…
Learning Parametrised Graph Shift Operators
George Dasoulas, Johannes Lutzeyer, Michalis Vazirgiannis
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, im…