3 papers
cs.LG2025
Towards a General Framework for Predicting and Explaining the Hardness of Graph-based Combinatorial Optimization Problems using Machine Learning and Association Rule Mining
Bharat Sharman, Elkafi Hassini
This study introduces GCO-HPIF, a general machine-learning-based framework to predict and explain the computational hardness of combinatorial optimization problems that can be repr…
cs.DS2025
Comparative algorithm performance evaluation and prediction for the maximum clique problem using instance space analysis
Bharat Sharman, Elkafi Hassini
The maximum clique problem, a well-known graph-based combinatorial optimization problem, has been addressed through various algorithmic approaches, though systematic analyses of th…
cs.LG2025
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
Bharat Sharman, Elkafi Hassini
Numerous graph neural network (GNN)-based algorithms have been proposed to solve graph-based combinatorial optimization problems (COPs), but methods to explain their predictions re…