New classes of clustering coefficient locally maximizing graphs

Tatsuya Fukami, Norikazu Takahashi

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

A simple connected undirected graph G is called a clustering coefficient locally maximizing graph if its clustering coefficient is not less than that of any simple connected graph obtained from G by rewiring an edge, that is, removing an edge and adding a new edge. In this paper, we present some new classes of clustering coefficient locally maximizing graphs. We first show that any graph composed of multiple cliques with orders greater than two sharing one vertex is a clustering coefficient locally maximizing graph. We next show that any graph obtained from a tree by replacing edges with cliques with the same order other than four is a clustering coefficient locally maximizing graph. We also extend the latter result to a more general class.

Original languageEnglish
Pages (from-to)202-213
Number of pages12
JournalDiscrete Applied Mathematics
Volume162
DOIs
Publication statusPublished - Jan 10 2014

Fingerprint

Clustering Coefficient
Graph in graph theory
Clique
Connected graph
Simple Graph
Undirected Graph
Class
Sharing
Vertex of a graph

Keywords

  • Clustering coefficient
  • Complex network
  • Connected caveman graph

ASJC Scopus subject areas

  • Applied Mathematics
  • Discrete Mathematics and Combinatorics

Cite this

New classes of clustering coefficient locally maximizing graphs. / Fukami, Tatsuya; Takahashi, Norikazu.

In: Discrete Applied Mathematics, Vol. 162, 10.01.2014, p. 202-213.

Research output: Contribution to journalArticle

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