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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.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…