13 papers
Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need
Nathan Painchaud, Tristan Habémont, Morgane des Ligneris +6
Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records, parameters measured from c…
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2
We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces a true graph-level latent spac…
If You Want to Be Robust, Be Wary of Initialization
Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis +1
Graph Neural Networks (GNNs) have demonstrated remarkable performance across a spectrum of graph-related tasks, however concerns persist regarding their vulnerability to adversaria…
Enhancing Graph Classification Robustness with Singular Pooling
Sofiane Ennadir, Oleg Smirnov, Yassine Abbahaddou +2
Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remain…
Efficient Data Selection for Training Genomic Perturbation Models
George Panagopoulos, Johannes F. Lutzeyer, Sofiane Ennadir +2
Genomic studies face a vast hypothesis space, while interventions such as gene perturbations remain costly and time-consuming. To accelerate such experiments, gene perturbation mod…
SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network
Zhipu Cui, Johannes Lutzeyer
Graph Neural Networks (GNNs) have achieved remarkable success across a range of learning tasks. However, scaling GNNs to large graphs remains a significant challenge, especially fo…