26 citations · 44 across the 6 of their papers we have counts for
9 papers
LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa +3
Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Optimization Model for Subspac…
Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
Existing multiplex graph models often assume homophily, where connected nodes tend to belong to the same class or share similar attributes. Consequently, these models may struggle…
A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions
Mohamed Mahmoud Amar, Nairouz Mrabah, Mohamed Bouguessa +1
Graph Self-Supervised Learning (GSSL) has emerged as a powerful paradigm for generating high-quality representations for graph-structured data. While multi-scale graph contrastive…
Scalable Deep Subspace Clustering Network
Nairouz Mrabah, Mohamed Bouguessa, Sihem Sami
Subspace clustering methods face inherent scalability limits due to the cost (with denoting the number of data samples) of constructing full affinities and…
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal +2
Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) meth…
Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation
Nairouz Mrabah, Nicolas Richet, Ismail Ben Ayed +1
Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the…
A Geometric Perspective for High-Dimensional Multiplex Graphs
Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
High-dimensional multiplex graphs are characterized by their high number of complementary and divergent dimensions. The existence of multiple hierarchical latent relations between…
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
We investigate the problem of multiplex graph embedding, that is, graphs in which nodes interact through multiple types of relations (dimensions). In recent years, several methods…
A Contrastive Variational Graph Auto-Encoder for Node Clustering
Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
Variational Graph Auto-Encoders (VGAEs) have been widely used to solve the node clustering task. However, the state-of-the-art methods have numerous challenges. First, existing VGA…