4 citations · 22 across the 28 of their papers we have counts for
28 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 encodes entire graphs into single, fixed-dimensional latent vectors rather than per-node embeddin…
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…
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…
ADMP-GNN: Adaptive Depth Message Passing GNN
Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer +1
Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing step…
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Sohir Maskey, Raffaele Paolino, Fabian Jogl +2
Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We intr…
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…
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…
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…
Graph Neural Networks on Discriminative Graphs of Words
Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis
In light of the recent success of Graph Neural Networks (GNNs) and their ability to perform inference on complex data structures, many studies apply GNNs to the task of text classi…
Understanding Virtual Nodes: Oversquashing and Node Heterogeneity
Joshua Southern, Francesco Di Giovanni, Michael Bronstein +1
While message passing neural networks (MPNNs) have convincing success in a range of applications, they exhibit limitations such as the oversquashing problem and their inability to…
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…
Graph Neural Machine: A New Model for Learning with Tabular Data
Giannis Nikolentzos, Siyun Wang, Johannes Lutzeyer +1
In recent years, there has been a growing interest in mapping data from different domains to graph structures. Among others, neural network models such as the multi-layer perceptro…
Path Neural Networks: Expressive and Accurate Graph Neural Networks
Gaspard Michel, Giannis Nikolentzos, Johannes Lutzeyer +1
Graph neural networks (GNNs) have recently become the standard approach for learning with graph-structured data. Prior work has shed light into their potential, but also their limi…
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…
Improving Graph Neural Networks at Scale: Combining Approximate PageRank and CoreRank
Ariel R. Ramos Vela, Johannes F. Lutzeyer, Anastasios Giovanidis +1
Graph Neural Networks (GNNs) have achieved great successes in many learning tasks performed on graph structures. Nonetheless, to propagate information GNNs rely on a message passin…
Graph Ordering Attention Networks
Michail Chatzianastasis, Johannes F. Lutzeyer, George Dasoulas +1
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-p…
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection pr…
Node Feature Kernels Increase Graph Convolutional Network Robustness
Mohamed El Amine Seddik, Changmin Wu, Johannes F. Lutzeyer +1
The robustness of the much-used Graph Convolutional Networks (GCNs) to perturbations of their input is becoming a topic of increasing importance. In this paper, the random GCN is i…
Sparsifying the Update Step in Graph Neural Networks
Johannes F. Lutzeyer, Changmin Wu, Michalis Vazirgiannis
Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural Network (GNN) framework, celebrate much success in the analysis of graph-structured data. Concurrently, the…
Learning Parametrised Graph Shift Operators
George Dasoulas, Johannes Lutzeyer, Michalis Vazirgiannis
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, im…