Coverage Centrality Maximization in Undirected Networks

TY - JOUR

T1 - Coverage Centrality Maximization in Undirected Networks

AU - Severini , Lorenzo

AU - Olsen, Martin

AU - D'Angelo, Gianlorenzo

N1 - Conference code: 33

PY - 2019

Y1 - 2019

N2 - Centrality metrics are among the main tools in social network analysis. Being central for a user of a network leads to several benefits to the user: central users are highly influential and play key roles within the network. Therefore, the optimization problem of increasing the centrality of a network user recently received considerable attention. Given a network and a target user v, the centrality maximization problem consists in creating k new links incident to v in such a way that the centrality of v is maximized, according to some centrality metric. Most of the algorithms proposed in the literature are based on showing that a given centrality metric is monotone and submodular with respect to link addition. However, this property does not hold for several shortest-path based centrality metrics if the links are undirected. In this paper we study the centrality maximization problem in undirected networks for one of the most important shortest-path based centrality measures, the coverage centrality. We provide several hardness and approximation results. We first show that the problem cannot be approximated within a factor greater than 1 - 1/e, unless P = NP, and, under the stronger gap-ETH hypothesis, the problem cannot be approximated within a factor better than 1/n o (1), where n is the number of users. We then propose two greedy approximation algorithms, and show that, by suitably combining them, we can guarantee an approximation factor of ?(1/vn). We experimentally compare the solutions provided by our approximation algorithm with optimal solutions computed by means of an exact IP formulation. We show that our algorithm produces solutions that are very close to the optimum.

AB - Centrality metrics are among the main tools in social network analysis. Being central for a user of a network leads to several benefits to the user: central users are highly influential and play key roles within the network. Therefore, the optimization problem of increasing the centrality of a network user recently received considerable attention. Given a network and a target user v, the centrality maximization problem consists in creating k new links incident to v in such a way that the centrality of v is maximized, according to some centrality metric. Most of the algorithms proposed in the literature are based on showing that a given centrality metric is monotone and submodular with respect to link addition. However, this property does not hold for several shortest-path based centrality metrics if the links are undirected. In this paper we study the centrality maximization problem in undirected networks for one of the most important shortest-path based centrality measures, the coverage centrality. We provide several hardness and approximation results. We first show that the problem cannot be approximated within a factor greater than 1 - 1/e, unless P = NP, and, under the stronger gap-ETH hypothesis, the problem cannot be approximated within a factor better than 1/n o (1), where n is the number of users. We then propose two greedy approximation algorithms, and show that, by suitably combining them, we can guarantee an approximation factor of ?(1/vn). We experimentally compare the solutions provided by our approximation algorithm with optimal solutions computed by means of an exact IP formulation. We show that our algorithm produces solutions that are very close to the optimum.

U2 - 10.1609/aaai.v33i01.3301501

DO - 10.1609/aaai.v33i01.3301501

M3 - Journal article

SN - 2374-3468

VL - 33

SP - 501

EP - 508

JO - Proceedings of the AAAI Conference on Artificial Intelligence

JF - Proceedings of the AAAI Conference on Artificial Intelligence

IS - 1

T2 - The Thirty-Third Conference on Artificial Intelligence

Y2 - 27 January 2019 through 1 February 2019

ER -