26 citations · 57 across the 13 of their papers we have counts for
15 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…
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…
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…
Graph Attention Network for Camera Relocalization on Dynamic Scenes
Mohamed Amine Ouali, Mohamed Bouguessa, Riadh Ksantini
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previou…
TopoDetect: Framework for Topological Features Detection in Graph Embeddings
Maroun Haddad, Mohamed Bouguessa
TopoDetect is a Python package that allows the user to investigate if important topological features, such as the Degree of the nodes, their Triangle Count, or their Local Clusteri…
Modeling Regime Shifts in Multiple Time Series
Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang
We investigate the problem of discovering and modeling regime shifts in an ecosystem comprising multiple time series known as co-evolving time series. Regime shifts refer to the ch…
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati +1
Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlook…
Exploring the Representational Power of Graph Autoencoder
Maroun Haddad, Mohamed Bouguessa
While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddi…
Context Matters: Self-Attention for Sign Language Recognition
Fares Ben Slimane, Mohamed Bouguessa
This paper proposes an attentional network for the task of Continuous Sign Language Recognition. The proposed approach exploits co-independent streams of data to model the sign lan…
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data po…
A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks
Ziwei He, Etienne Gael Tajeuna, Shengrui Wang +1
In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several ap…
Detecting Large Concept Extensions for Conceptual Analysis
Louis Chartrand, Jackie C. K. Cheung, Mohamed Bouguessa
When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit.…