research-article

Greedily Improving Our Own Closeness Centrality in a Network

Authors:

Pierluigi Crescenzi,

Gianlorenzo D'angelo,

Lorenzo Severini,

Yllka VelajAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 1

Article No.: 9, Pages 1 - 32

https://doi.org/10.1145/2953882

Published: 20 July 2016 Publication History

Abstract

The closeness centrality is a well-known measure of importance of a vertex within a given complex network. Having high closeness centrality can have positive impact on the vertex itself: hence, in this paper we consider the optimization problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it. We will consider both the undirected and the directed graph cases. In both cases, we first prove that the optimization problem does not admit a polynomial-time approximation scheme (unless P = NP), and then propose a greedy approximation algorithm (with an almost tight approximation ratio), whose performance is then tested on synthetic graphs and real-world networks.

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Index Terms

Greedily Improving Our Own Closeness Centrality in a Network
1. Applied computing
  1. Law, social and behavioral sciences
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 1

February 2017

288 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2974720

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2016 ACM.

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Publication History

Published: 20 July 2016

Accepted: 01 June 2016

Revised: 01 May 2016

Received: 01 September 2015

Published in TKDD Volume 11, Issue 1

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Teng SXie JZhang FLu CFang JWang KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Optimizing Network Resilience via Vertex AnchoringProceedings of the ACM Web Conference 202410.1145/3589334.3645465(606-617)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645465
Lu ZZhou XZehmakan AZhang Z(2024)Resistance Eccentricity in Graphs: Distribution, Computation and Optimization2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00315(4113-4126)Online publication date: 13-May-2024
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