Structural patterns in complex networks through spectral analysis

Ernesto Estrada

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
85 Downloads (Pure)

Abstract

The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network.
Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
PublisherSpringer
Pages43-59
Number of pages17
Volume6218
ISBN (Print)978-3-642-14979-5
DOIs
Publication statusPublished - 28 Aug 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6218

    Fingerprint

Keywords

  • subgraph centrality
  • Estrada index
  • network communities
  • communicability

Cite this

Estrada, E. (2010). Structural patterns in complex networks through spectral analysis. In Structural, Syntactic, and Statistical Pattern Recognition (Vol. 6218, pp. 43-59). (Lecture Notes in Computer Science; Vol. 6218). Springer. https://doi.org/10.1007/978-3-642-14980-1_4