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Application of semidefinite programming to maximize the spectral gap produced by node removal

  • Japan Science and Technology Agency
  • University of Hyogo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

The smallest positive eigenvalue of the Laplacian of a network is called the spectral gap and characterizes various dynamics on networks. We propose mathematical programming methods to maximize the spectral gap of a given network by removing a fixed number of nodes. We formulate relaxed versions of the original problem using semidefinite programming and apply them to example networks.

Original languageEnglish
Title of host publicationComplex Networks IV
Subtitle of host publicationProceedings of the 4th Workshop on Complex Networks CompleNet 2013
PublisherSpringer Verlag
Pages155-163
Number of pages9
ISBN (Print)9783642368431
DOIs
StatePublished - 2013

Publication series

NameStudies in Computational Intelligence
Volume476
ISSN (Print)1860-949X

Keywords

  • Combinatorial optimization
  • Eigenvalue
  • Laplacian
  • Network
  • Opinion formation
  • Random walk
  • Synchronization

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