2 citations · 6 across the 13 of their papers we have counts for
12 papers · 1 filter
Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature
Rachid Caich, Yassine Abbahaddou
Curvature notions on graphs, particularly Ollivier-Ricci and Forman, have emerged as powerful tools for addressing fundamental issues in Graph Neural Networks (GNNs) such as oversm…
Which Graph Shift Operator? A Spectral Answer to an Empirical Question
Yassine Abbahaddou
Graph Neural Networks (GNNs) have established themselves as the leading models for learning on graph-structured data, generally categorized into spatial and spectral approaches. Ce…
Key Principles of Graph Machine Learning: Representation, Robustness, and Generalization
Yassine Abbahaddou
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations from structured data. Despite their growing popularity and success across various applicati…
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…
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…
Grassmannian Geometry Meets Dynamic Mode Decomposition in DMD-GEN: A New Metric for Mode Collapse in Time Series Generative Models
Amime Mohamed Aboussalah, Yassine Abbahaddou
Generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often fail to capture the full diversity of their training data, leading to mode c…