Graph Algorithms and Combinatorial Optimisation
How can structural information and machine learning make hard graph problems more tractable while preserving clear objectives and measurable performance?
Research focus
Many questions about complex networks lead to computationally difficult graph problems. We investigate hybrid approaches in which a solver stays in charge and learning helps it decide where to search, which elements to prune or which intervention to evaluate next.
Our papers cover search-space reduction for maximum-clique problems and node- or edge-based network dismantling. Across these settings, we are interested in scalability, comparison against strong algorithmic baselines and what the learned strategy reveals about the structure of the problem.
Selected publications
- Learning fine-grained search space pruning and heuristics for combinatorial optimization, Journal of Heuristics (2023)
- Geometric deep learning sub-network extraction for maximum clique enumeration, PLOS ONE (2024)
- Edge Dismantling with Geometric Reinforcement Learning, Complex Networks (2024)