55 citations · 215 across the 34 of their papers we have counts for
14 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…
Centrality Graph Shift Operators for Graph Neural Networks
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer +1
Graph Shift Operators (GSOs), such as the adjacency and graph Laplacian matrices, play a fundamental role in graph theory and graph representation learning. Traditional GSOs are ty…
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Jhony H. Giraldo, Aref Einizade, Andjela Todorovic +4
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-sca…
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer +2
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribut…
The Factuality of Large Language Models in the Legal Domain
Rajaa El Hamdani, Thomas Bonald, Fragkiskos Malliaros +2
This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…