Identifying optimal targets of network attack by belief propagation

TL;DR

This paper applies a belief propagation-guided decimation algorithm to identify minimal node sets for network disruption, outperforming previous methods and causing abrupt network collapse, with implications for network security.

Contribution

The study demonstrates the effectiveness of belief propagation-guided decimation in solving the network attack problem, improving upon existing algorithms for various network types.

Findings

BPD algorithm outperforms Collective Information algorithm.

BPD induces abrupt network collapse.

Algorithm has approximately linear time complexity.

Abstract

For a network formed by nodes and undirected links between pairs of nodes, the network optimal attack problem aims at deleting a minimum number of target nodes to break the network down into many small components. This problem is intrinsically related to the feedback vertex set problem that was successfully tackled by spin glass theory and an associated belief propagation-guided decimation (BPD) algorithm [H.-J. Zhou, Eur. Phys. J. B 86 (2013) 455]. In the present work we apply the BPD alrogithm (which has approximately linear time complexity) to the network optimal attack problem, and demonstrate that it has much better performance than a recently proposed Collective Information algorithm [F. Morone and H. A. Makse, Nature 524 (2015) 63--68] for different types of random networks and real-world network instances. The BPD-guided attack scheme often induces an abrupt collapse of the whole network, which may make it very difficult to defend.