28 citations · 51 across the 6 of their papers we have counts for
3 papers · 1 filter
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Pál András Papp, Karolis Martinkus, Lukas Faber +1
This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs…
Should Graph Neural Networks Use Features, Edges, Or Both?
Lukas Faber, Yifan Lu, Roger Wattenhofer
Graph Neural Networks (GNNs) are the first choice for learning algorithms on graph data. GNNs promise to integrate (i) node features as well as (ii) edge information in an end-to-e…
Towards Robust Graph Contrastive Learning
Nikola Jovanović, Zhao Meng, Lukas Faber +1
We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial rob…