NETBREAKER: Benchmarking Network Dismantling across Objectives, Scales, and Topologies

Network dismantling seeks to fragment a network with as few node removals as possible, which is widely applied in vulnerability analysis, epidemic containment, and infrastructure protection. Although learning-based methods have become increasingly prominent, their progress remains difficult to assess because existing studies differ in method coverage, datasets, evaluation criteria, and experimental protocols. Thus, we introduce NETBREAKER, a comprehensive benchmark designed for the learning era of network dismantling. NETBREAKER organizes 30 methods (18 non-learning and 12 learning-based) under a common taxonomy and evaluates them on 185 networks from eight unified domains, ranging from 14 to 1.69 million nodes. To enable fair comparison, heterogeneous method outputs are converted into node removal sequences and assessed under a unified execution protocol. The evaluation covers four complementary dimensions: target-based effectiveness, trajectory-based effectiveness, structural diagnostics, and applicability & computational efficiency. Extensive experiments show that current learning-based methods do not consistently outperform strong non-learning approaches, while exposing substantial differences in effectiveness, scalability, and efficiency across network conditions and evaluation perspectives. NETBREAKER provides a reproducible basis for measuring progress and developing reliable, scalable, and computationally efficient network dismantling methods.