Strategic Evasion of Centrality Measures

TL;DR

This paper models the strategic manipulation of social network centrality measures as a Bayesian Stackelberg game, analyzing how seekers can optimize detection of evaders who rewire connections to hide their importance.

Contribution

It introduces a game-theoretic framework for understanding strategic evasion of centrality measures, analyzing equilibria and optimization problems in this context.

Findings

Identifies which centralities are most effective for detection

Analyzes the complexity of evasion strategies

Provides first theoretical insights into strategic evasion in networks

Abstract

Among the most fundamental tools for social network analysis are centrality measures, which quantify the importance of every node in the network. This centrality analysis typically disregards the possibility that the network may have been deliberately manipulated to mislead the analysis. To solve this problem, a recent study attempted to understand how a member of a social network could rewire the connections therein to avoid being identified as a leader of that network. However, the study was based on the assumption that the network analyzer - the seeker - is oblivious to any evasion attempts by the evader. In this paper, we relax this assumption by modelling the seeker and evader as strategic players in a Bayesian Stackelberg game. In this context, we study the complexity of various optimization problems, and analyze the equilibria of the game under different assumptions, thereby drawing the first conclusions in the literature regarding which centralities the seeker should use to maximize the chances of detecting a strategic evader.