Influence Robustness of Nodes in Multiplex Networks against Attacks

TL;DR

This paper introduces MultiCoreRank, a new measure for assessing node influence in multiplex networks, and analyzes how network structure affects node robustness against targeted attacks.

Contribution

The paper proposes MultiCoreRank for influence measurement and compares influence robustness across different multiplex network structures.

Findings

Assortative networks are more resilient to attacks.

Disassortative networks break down faster under attack.

Structural features influence node influence robustness.

Abstract

Recent advances have focused mainly on the resilience of the monoplex network in attacks targeting random nodes or links, as well as the robustness of the network against cascading attacks. However, very little research has been done to investigate the robustness of nodes in multiplex networks against targeted attacks. In this paper, we first propose a new measure, MultiCoreRank, to calculate the global influence of nodes in a multiplex network. The measure models the influence propagation on the core lattice of a multiplex network after the core decomposition. Then, to study how the structural features can affect the influence robustness of nodes, we compare the dynamics of node influence on three types of multiplex networks: assortative, neutral, and disassortative, where the assortativity is measured by the correlation coefficient of the degrees of nodes across different layers. We found that assortative networks have higher resilience against attack than neutral and disassortative networks. The structure of disassortative networks tends to break down quicker under attack.