Network growth with preferential attachment for high indegree and low outdegree

Sevi̇m, Volkan; Rikvold, Per Arne

doi:10.1016/j.physa.2008.01.034

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…In [45] the authors extended [6] and proposed a more general model, where preference is proportional to a positive power of the ratio of in-degree to out-degree. Although similar, the model we investigate in this paper is inherently different, as preference is inversely proportional to both node degree and node distance.…”

Section: Mathematical Models and Preferential Attachmentmentioning

confidence: 99%

Design and Analysis of Distributed Tree Growing Algorithms

et al. 2022

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Tree-based systems rely on real-time dissemination trees to deliver information to nodes. In order to offer good services, two fundamental aspects should guide the real-time growth process: low node degree and short distances to the server. In this paper, we propose a growth process to construct trees and make a detailed study on modeling and performance analysis of these tree-based systems. Our generative mechanism is based on the preferential attachment principle, where preference is given in terms of node quality. The proposed growth mechanism has a single parameter to weigh the relative importance of node degree and node distance on assessing node quality. We aim at understanding this mechanism when considering the local aspect of the node's degree and the global aspect of the distance to a source. With this goal, we investigate our model through simulations and compare it to other growth processes. Our results indicate that the proposed model is capable of self-organizing nodes into good trees under six metrics of interest.INDEX TERMS Preferential attachment, Real-time growth process, Tree-based systems, Node quality, Recursive tree, Random tree, Random recursive tree, Power of two choices.

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Section: Mathematical Models and Preferential Attachmentmentioning

confidence: 99%

Design and Analysis of Distributed Tree Growing Algorithms

et al. 2022

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“…Linear de-preferential urn models 1177 Recently, there has been some interest in random graphs [9], [50], [51], where attachment probabilities of a new vertex are decreasing functions of the degree of the existing vertices. In most cases, such models also lead to negative reinforcement.…”

Section: Background and Motivationmentioning

confidence: 99%

Linear de-preferential urn models

Bandyopadhyay

Kaur

2018

Adv. Appl. Probab.

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In this paper we consider a new type of urn scheme, where the selection probabilities are proportional to a weight function, which is linear but decreasing in the proportion of existing colours. We refer to it as the de-preferential urn scheme. We establish the almost-sure limit of the random configuration for any balanced replacement matrix R. In particular, we show that the limiting configuration is uniform on the set of colours if and only if R is a doubly stochastic matrix. We further establish the almost-sure limit of the vector of colour counts and prove central limit theorems for the random configuration as well as for the colour counts.

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Network growth with preferential attachment for high indegree and low outdegree

Cited by 2 publications

References 23 publications

Design and Analysis of Distributed Tree Growing Algorithms

Design and Analysis of Distributed Tree Growing Algorithms

Linear de-preferential urn models

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