Graphs that locally maximize clustering coefficient in the space of graphs with a fixed degree sequence

Tatsuya Fukami, Norikazu Takahashi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper studies the problem of finding graphs that locally maximize the clustering coefficient in the space of graphs with a fixed degree sequence. Such a graph is characterized by the property that the clustering coefficient cannot be increased, no matter how a single 2-switch is applied. First, an explicit formula for the amount of change in the clustering coefficient of a graph caused by a single 2-switch is given. Next, some classes of graphs with the property stated above are presented. An example of such a graph is the one obtained from a tree by replacing its edges with cliques with the same order.

Original languageEnglish
Pages (from-to)525-535
Number of pages11
JournalDiscrete Applied Mathematics
Volume217
DOIs
Publication statusPublished - Jan 30 2017

Fingerprint

Clustering Coefficient
Degree Sequence
Maximise
Switches
Graph in graph theory
Switch
Clique
Explicit Formula

Keywords

  • 2-switch
  • Clustering coefficient
  • Complex network

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Applied Mathematics

Cite this

Graphs that locally maximize clustering coefficient in the space of graphs with a fixed degree sequence. / Fukami, Tatsuya; Takahashi, Norikazu.

In: Discrete Applied Mathematics, Vol. 217, 30.01.2017, p. 525-535.

Research output: Contribution to journalArticle

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