105 citations · 270 across the 20 of their papers we have counts for
4 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…
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…
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels
Tiago Santos, Florian Lemmerich, Denis Helic
As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay h…
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…