Heuristic algorithms for the stochastic critical node detection problem
This work addresses network vulnerability assessment for applications like transportation and epidemic control, but it is incremental as it extends existing critical node detection to a stochastic version with new algorithms.
The paper tackled the stochastic critical node detection problem, where edge existence is probabilistic, by proposing heuristic and learning-based methods, with experiments on random graphs showing heuristic methods achieve strong scalability and learning-based methods maintain constant inference time.
Given a network, the critical node detection problem finds a subset of nodes whose removal disrupts the network connectivity. Since many real-world systems are naturally modeled as graphs, assessing the vulnerability of the network is essential, with applications in transportation systems, traffic forecasting, epidemic control, and biological networks. In this paper, we consider a stochastic version of the critical node detection problem, where the existence of edges is given by certain probabilities. We propose heuristics and learning-based methods for the problem and compare them with existing algorithms. Experimental results performed on random graphs from small to larger scales, with edge-survival probabilities drawn from different distributions, demonstrate the effectiveness of the methods. Heuristic methods often illustrate the strongest results with high scalability, while learning-based methods maintain nearly constant inference time as the network size and density grow.