Hiding in Multilayer Networks

TL;DR

This paper analyzes the robustness of centrality measures in multilayer networks against strategic manipulation, proving computational hardness and empirically evaluating heuristics to understand evasion strategies.

Contribution

It is the first to analyze how centrality measures in multilayer networks resist strategic evasion, establishing NP-completeness and comparing robustness across measures.

Findings

Centrality measures based on entire network topology are more robust.

Determining optimal evasion strategies is NP-complete.

Heuristics can effectively approximate evasion tactics.

Abstract

Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an "evader" who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking-thereby reducing the chance of being highlighted as a key figure in the network-while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to "hide" is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.