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Greedily Improving Our Own Centrality in A Network

  • Pierluigi Crescenzi
  • Gianlorenzo D’Angelo
  • Lorenzo Severini
  • Yllka Velaj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9125)

Abstract

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

Keywords

Short Path Greedy Algorithm Random Graph Approximation Ratio Centrality Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pierluigi Crescenzi
    • 1
  • Gianlorenzo D’Angelo
    • 2
  • Lorenzo Severini
    • 2
  • Yllka Velaj
    • 2
  1. 1.Department of Information EngineeringUniversity of FlorenceFlorenceItaly
  2. 2.Gran Sasso Science Institute (GSSI)L’AquilaItaly

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