Greedily Improving Our Own Closeness Centrality in a Network

Author:

Crescenzi Pierluigi1,D'angelo Gianlorenzo2ORCID,Severini Lorenzo2,Velaj Yllka2

Affiliation:

1. University of Florence, Florence, Italy

2. Gran Sasso Science Institute, L'Aquila, Italy

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.

Funder

14th International Symposium on Experimental Algorithms

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference49 articles.

1. Arnetminer. Accessed January 15 2015 from http://arnetminer.org. Arnetminer. Accessed January 15 2015 from http://arnetminer.org.

2. The Effect of New Links on Google Pagerank

3. Supervised random walks

4. The Shortcut Problem - Complexity and Algorithms

Cited by 50 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Adversarial analysis of similarity-based sign prediction;Artificial Intelligence;2024-10

2. Dendrobium nobile alkaloids modulate calcium dysregulation and neuroinflammation in Alzheimer's disease: A bioinformatic analysis;Pharmacological Research - Modern Chinese Medicine;2024-09

3. Tackling school segregation with transportation network interventions: an agent-based modelling approach;Autonomous Agents and Multi-Agent Systems;2024-05-20

4. Resistance Eccentricity in Graphs: Distribution, Computation and Optimization;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

5. Mapping the entrepreneurship ecosystem scholarship: current state and future directions;International Entrepreneurship and Management Journal;2024-05-02

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3