Optimizing edge weights in the inverse eigenvector centrality problem

TL;DR

This paper addresses the inverse eigenvector centrality problem on directed graphs by characterizing feasible weights and proposing six optimization strategies, demonstrated on real social networks to show their structural impacts.

Contribution

It introduces a systematic framework for solving the inverse eigenvector centrality problem with multiple strategies, enabling flexible network reconstruction and design.

Findings

Different strategies produce distinct weighted network structures.

The framework effectively preserves prescribed centrality profiles.

Applications demonstrated on real-world social networks.

Abstract

In this paper we study the inverse eigenvector centrality problem on directed graphs: given a prescribed node centrality profile, we seek edge weights that realize it. Since this inverse problem generally admits infinitely many solutions, we explicitly characterize the feasible set of admissible weights and introduce six optimization problems defined over this set, each corresponding to a different weight-selection strategy. These formulations provide representative solutions of the inverse problem and enable a systematic comparison of how different strategies influence the structure of the resulting weighted networks. We illustrate our framework using several real-world social network datasets, showing that different strategies produce different weighted graph structures while preserving the prescribed centrality. The results highlight the flexibility of the proposed approach and its potential applications in network reconstruction, and network design or network manipulation.