Nairouz Mrabah

According to our database, Nairouz Mrabah authored at least 14 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Rethinking deep clustering paradigms: Self-supervision is all you need.

Amal Shaheen, Nairouz Mrabah, Riadh Ksantini, Abdulla Alqaddoumi

Neural Networks, 2025

2024

Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding.

Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa

IEEE Trans. Knowl. Data Eng., April, 2024

A contrastive variational graph auto-encoder for node clustering.

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

Pattern Recognit., 2024

A Geometric Perspective for High-Dimensional Multiplex Graphs.

Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering.

Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini

IEEE Trans. Knowl. Data Eng., September, 2023

Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering.

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data.

Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (Extended abstract).

Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Adversarial Deep Embedded Clustering: On a better trade-off between Feature Randomness and Feature Drift (Extended abstract).

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective.

Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift.

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

IEEE Trans. Knowl. Data Eng., 2022

Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering.

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2020

Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction.

Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini, Zied Lachiri

Neural Networks, 2020

2019

Deep Clustering with a Dynamic Autoencoder.

Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini

CoRR, 2019