2 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns…
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to advers…
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. However, concerns have arisen regarding the vulnerability of GNNs…
Approximating Score-based Explanation Techniques Using Conformal Regression
Amr Alkhatib, Henrik Boström, Sofiane Ennadir +1
Score-based explainable machine-learning techniques are often used to understand the logic behind black-box models. However, such explanation techniques are often computationally e…