Statistical properties of a generalized threshold network model

Yusuke Ide; Norio Konno; Naoki Masuda

doi:10.1007/s11009-008-9111-5

Statistical properties of a generalized threshold network model

Yusuke Ide
, Norio Konno
, Naoki Masuda

Mathematics

Yokohama National University

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

The threshold network model is a type of finite random graph. In this paper, we introduce a generalized threshold network model. A pair of vertices with random weights is connected by an edge when real-valued functions of the pair of weights belong to given Borel sets. We extend several known limit theorems for the number of prescribed subgraphs and prove a uniform strong law of large numbers. We also prove two limit theorems for the local and global clustering coefficients.

Original language	English
Pages (from-to)	361-377
Number of pages	17
Journal	Methodology and Computing in Applied Probability
Volume	12
Issue number	3
DOIs	https://doi.org/10.1007/s11009-008-9111-5
State	Published - 2010

Keywords

Complex networks
Random graphs
Threshold network models

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10.1007/s11009-008-9111-5

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abstract = "The threshold network model is a type of finite random graph. In this paper, we introduce a generalized threshold network model. A pair of vertices with random weights is connected by an edge when real-valued functions of the pair of weights belong to given Borel sets. We extend several known limit theorems for the number of prescribed subgraphs and prove a uniform strong law of large numbers. We also prove two limit theorems for the local and global clustering coefficients.",

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AU - Ide, Yusuke

AU - Konno, Norio

AU - Masuda, Naoki

PY - 2010

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AB - The threshold network model is a type of finite random graph. In this paper, we introduce a generalized threshold network model. A pair of vertices with random weights is connected by an edge when real-valued functions of the pair of weights belong to given Borel sets. We extend several known limit theorems for the number of prescribed subgraphs and prove a uniform strong law of large numbers. We also prove two limit theorems for the local and global clustering coefficients.

KW - Complex networks

KW - Random graphs

KW - Threshold network models

UR - https://www.scopus.com/pages/publications/77954426096

U2 - 10.1007/s11009-008-9111-5

DO - 10.1007/s11009-008-9111-5

M3 - Article

AN - SCOPUS:77954426096

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SP - 361

EP - 377

JO - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

IS - 3

ER -

Statistical properties of a generalized threshold network model

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Keywords

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