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.
no code implementations • 2 Mar 2020 • Changmin Wu, Giannis Nikolentzos, Michalis Vazirgiannis
Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology.