Node Feature Kernels Increase Graph Convolutional Network Robustness

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.

Node Classification

Sparsifying the Update Step in Graph Neural Networks

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.

Analysing the Update step in Graph Neural Networks via Sparsification

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.

EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

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.

Graph Mining