26 citations · 57 across the 13 of their papers we have counts for
9 papers · 1 filter
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 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…
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…
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…