A family of network evolution models with moderate density

Abstract We study a parametrized family of discrete time network evolution models. The evolution of the graph is based on constructions and deletions of k -cliques. Using martingale theory, we prove an almost sure limit theorem for the number of vertices, then show its asymptotic normality. We obtain an almost sure limit theorem for the degree of a fixed vertex. We also present an asymptotic normality result for the degree of a fixed vertex. Functional limit theorem is obtained for the number of vertices. Multidimensional functional limit theorem is proved for the joint behaviour of the degrees of several fixed vertices. For the proof, we apply martingale functional limit theorems. Besides mathematical proofs, we offer simulation results supporting our theorems.

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Publication Details

Journal
Statistical Papers
Published
2025-06-09
DOI
https://doi.org/10.1007/s00362-025-01712-y
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.32

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article

A family of network evolution models with moderate density

László Fórián, István Fazekas
1 citations
Statistical Papers
Complex Network Analysis Techniques
1.32
article

A family of network evolution models with moderate density

László Fórián, István Fazekas
article en
1 citations

Abstract

Abstract We study a parametrized family of discrete time network evolution models. The evolution of the graph is based on constructions and deletions of k -cliques. Using martingale theory, we prove an almost sure limit theorem for the number of vertices, then show its asymptotic normality. We obtain an almost sure limit theorem for the degree of a fixed vertex. We also present an asymptotic normality result for the degree of a fixed vertex. Functional limit theorem is obtained for the number of vertices. Multidimensional functional limit theorem is proved for the joint behaviour of the degrees of several fixed vertices. For the proof, we apply martingale functional limit theorems. Besides mathematical proofs, we offer simulation results supporting our theorems.

Statistical PapersVol. 66(5)
University of Debrecen (HU)
Debreceni Egyetem
Openalex Percentile: Top 19%
Complex Network Analysis Techniques
1.32
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