We study the network dismantling problem, which consists in determining a
minimal set of vertices whose removal leaves the network broken into connected
components of sub-extensive size. For a large class of random graphs, this
problem is tightly connected to the decycling problem (the removal of vertices
leaving the graph acyclic). Exploiting this connection and recent works on
epidemic spreading we present precise predictions for the minimal size of a
dismantling set in a large random graph with a prescribed (light-tailed) degree
distribution. Building on the statistical mechanics perspective we propose a
three-stage Min-Sum algorithm for efficiently dismantling networks, including
heavy-tailed ones for which the dismantling and decycling problems are not
equivalent. We also provide further insights into the dismantling problem
concluding that it is an intrinsically collective problem and that optimal
dismantling sets cannot be viewed as a collection of individually well
performing nodes.Comment: Source code and data can be found at
https://github.com/abraunst/decycle