26 citations · 53 across the 5 of their papers we have counts for
5 papers
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 Deep Learning Based Cost Model for Automatic Code Optimization
Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas +4
Enabling compilers to automatically optimize code has been a longstanding goal for the compiler community. Efficiently solving this problem requires using precise cost models. Thes…
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous +5
In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compile…