9 papers
When does distribution shift break graph neural networks calibration?
Abderaouf Bahi
Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable. However, how such shifts affect model calibration, defin…
Graph Neural Networks Applications Across Domains: All Insights You Need
Abderaouf Bahi
Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The interesting question is no…
NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities
Abderaouf Bahi, Amel Ourici
This paper investigates the feasibility of human mobility in extreme urban morphologies characterized by high-density vertical structures and linear city layouts. To assess whether…
Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
Abderaouf Bahi, Amel Ourici, Hasan Dincer +2
The exponential growth of digital services has positioned data centers among the most energy-intensive infrastructures in the modern economy, raising critical concerns regarding op…
MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations
Abderaouf Bahi, Mourad Boughaba, Ibtissem Gasmi +2
Capturing user intent across heterogeneous behavioral domains stands as a fundamental challenge in session-based recommender systems. Yet, existing multi-domain approaches frequent…
FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi +3
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require…