Maximizing Coverage Centrality via Network Design: Extended Version

TL;DR

This paper addresses the challenge of increasing coverage centrality in networks by adding edges, proposing algorithms with strong theoretical guarantees and demonstrating their effectiveness through extensive experiments.

Contribution

It introduces a greedy and sampling-based algorithm for coverage centrality maximization, with proven approximation bounds and scalability to large networks.

Findings

The greedy algorithm achieves near-optimal solutions under certain constraints.

Sampling-based method scales efficiently to large networks.

Empirical results confirm the effectiveness of proposed algorithms.

Abstract

Network centrality plays an important role in many applications. Central nodes in social networks can be influential, driving opinions and spreading news or rumors.In hyperlinked environments, such as the Web, where users navigate via clicks, central content receives high traffic, becoming targets for advertising campaigns. While there is an extensive amount of work on centrality measures and their efficient computation, controlling nodes' centrality via network updates is a more recent and challenging problem. Performing minimal modifications to a network to achieve a desired property falls under the umbrella of network design problems. This paper is focused on improving the coverage centrality of a set of nodes, which is the number of pairs of nodes that have a shortest path passing through the set, by adding edges to the network. We prove strong inapproximability results and propose a greedy algorithm for maximizing coverage centrality. To ensure scalability to large networks, we also design an efficient sampling algorithm for the problem. In addition to providing an extensive empirical evaluation of our algorithms, we also show that, under some realistic constraints, the proposed solutions achieve almost-optimal approximation for coverage centrality maximization.