Enforcing Katz and PageRank Centrality Measures in Complex Networks
Stefano Cipolla, Fabio Durastante, Beatrice Meini

TL;DR
This paper presents an optimization-based method to minimally modify network edge weights to enforce specific Katz and PageRank centrality measures while preserving the original network structure.
Contribution
It introduces a scalable optimization approach for adjusting network weights to achieve desired centrality scores, balancing structural integrity and targeted centrality enforcement.
Findings
Effective algorithms for minimal weight perturbations
Ability to control centrality measures in complex networks
Scalable solutions for large network modifications
Abstract
We investigate the problem of enforcing a desired centrality measure in complex networks, while still keeping the original pattern of the network. Specifically, by representing the network as a graph with suitable nodes and weighted edges, we focus on computing the smallest perturbation on the weights required to obtain a prescribed PageRank or Katz centrality index for the nodes. Our approach relies on optimization procedures that scale with the number of modified edges, enabling the exploration of different scenarios and altering network structure and dynamics.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpinion Dynamics and Social Influence
