Coverage Centrality Maximization in Undirected Networks

Lorenzo Severini , Martin Olsen, Gianlorenzo D'Angelo

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Abstract

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.

Original languageEnglish
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume33
Issue1
Pages (from-to)501-508
Number of pages8
ISSN2374-3468
DOIs
Publication statusPublished - 2019
EventThe Thirty-Third Conference on Artificial Intelligence: AAAI-19 - Hilton Hawaiian Village, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019
Conference number: 33

Conference

ConferenceThe Thirty-Third Conference on Artificial Intelligence
Number33
LocationHilton Hawaiian Village
Country/TerritoryUnited States
CityHonolulu
Period27/01/201901/02/2019

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