GRLND: A graph reinforcement learning framework for network dismantling

Network Dismantling (ND) seeks to identify the smallest subset of nodes whose removal fragments a network into disconnected components. Traditional methods rely on fixed centrality heuristics or supervised models trained on synthetic data, often failing to generalize across diverse topologies. We introduce GRLND, a Graph Reinforcement Learning framework that enables fully unsupervised, structure-aware dismantling through end-to-end optimization. GRLND formulates ND as a single-step Markov Decision Process (MDP), where the action is a binary mask indicating the nodes to be removed-allowing the agent to generate a complete dismantling strategy in a single forward pass while accounting for the joint effect of multiple node removals. The framework combines a Graph Convolutional Network (GCN) for topological encoding with a stochastic policy trained via the REINFORCE algorithm. Additionally, we design a task-specific reward that balances connectivity disruption and removal sparsity, guiding the policy toward compact yet high-impact dismantling solutions. Experiments on both synthetic and real-world networks show that GRLND consistently outperforms classical heuristics and recent learning-based methods, achieving strong generalization without requiring labels or pretraining.