Heuristic algorithms for the stochastic critical node detection problem

TL;DR

This paper addresses the stochastic critical node detection problem in networks, proposing heuristics and learning-based methods that outperform existing algorithms in scalability and inference time, with demonstrated effectiveness on various random graph models.

Contribution

It introduces new heuristic and learning-based algorithms for the stochastic critical node detection problem, enhancing scalability and inference efficiency.

Findings

Heuristic methods show strong results and high scalability.

Learning-based methods maintain nearly constant inference time.

Proposed methods outperform existing algorithms in experiments.

Abstract

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.