17 citations · 38 across the 7 of their papers we have counts for
5 papers · 1 filter
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
Hussain Hussain, Meng Cao, Sandipan Sikdar +4
We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a partic…
State-Space Constraints Can Improve the Generalisation of the Differentiable Neural Computer to Input Sequences With Unseen Length
Patrick Ofner, Roman Kern
Memory-augmented neural networks (MANNs) can perform algorithmic tasks such as sorting. However, they often fail to generalise to input sequence lengths not encountered during trai…
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +3
Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approaches are typically informed, i.e. they have access…
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +2
Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. I…
A Formally Robust Time Series Distance Metric
Maximilian Toller, Bernhard C. Geiger, Roman Kern
Distance-based classification is among the most competitive classification methods for time series data. The most critical component of distance-based classification is the selecte…