Quantum Statistics and Networks by Asymmetric Preferential Attachment of Nodes-between Bosons and Fermions

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

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Quantum statistics of a laser cooled ideal gas

Physical Review Letters ◽

10.1103/physrevlett.72.2977 ◽

1994 ◽

Vol 72 (19) ◽

pp. 2977-2980 ◽

Cited By ~ 45

Author(s):

J. I. Cirac ◽

M. Lewenstein ◽

P. Zoller

Keyword(s):

Ideal Gas ◽

Quantum Statistics

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The structure of co-publications multilayer network

Computational Social Networks ◽

10.1186/s40649-021-00089-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Ghislain Romaric Meleu ◽

Paulin Yonta Melatagia

Keyword(s):

Power Law ◽

Degree Distribution ◽

Preferential Attachment ◽

Small World ◽

Generation Model ◽

Small World Network ◽

Power Law Distribution ◽

Multilayer Networks ◽

Multilayer Network ◽

Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

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Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

Sustainability ◽

10.3390/su13031512 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1512

Author(s):

Yicheol Han ◽

Stephan J. Goetz ◽

Claudia Schmidt

Keyword(s):

Supply Chains ◽

Preferential Attachment ◽

Material Balance ◽

Service Industries ◽

Firm Location ◽

Commodity Flows ◽

Geographic Space ◽

Different Types ◽

Gravity Equations ◽

General Method

This article presents a spatial supply network model for estimating and visualizing spatial commodity flows that used data on firm location and employment, an input–output table of inter-industry transactions, and material balance-type equations. Building on earlier work, we proposed a general method for visualizing detailed supply chains across geographic space, applying the preferential attachment rule to gravity equations in the network context; we then provided illustrations for U.S. extractive, manufacturing, and service industries, also highlighting differences in rural–urban interdependencies across these sectors. The resulting visualizations may be helpful for better understanding supply chain geographies, as well as business interconnections and interdependencies, and to anticipate and potentially address vulnerabilities to different types of shocks.

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Scale free is not rare in international trade networks

Scientific Reports ◽

10.1038/s41598-021-92764-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Linqing Liu ◽

Mengyun Shen ◽

Chang Tan

Keyword(s):

International Trade ◽

Preferential Attachment ◽

Developed Countries ◽

Total Trade ◽

Trade Networks ◽

Scale Free ◽

Weighted Networks ◽

Free Exploration ◽

Technology Products ◽

Attachment Mechanism

AbstractFailing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

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Likelihood-based approach to discriminate mixtures of network models that vary in time

Scientific Reports ◽

10.1038/s41598-021-84085-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Naomi A. Arnold ◽

Raul J. Mondragón ◽

Richard G. Clegg

Keyword(s):

Network Model ◽

Change Point ◽

Network Science ◽

Preferential Attachment ◽

Network Models ◽

Explanatory Models ◽

Growth Mechanisms ◽

Artificial Data ◽

Network Growth ◽

Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

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What Influences Relationship Formation in a Global Civil Society Network? An Examination of Valued Multiplex Relations

Communication Research ◽

10.1177/00936502211016162 ◽

2021 ◽

pp. 009365022110161

Author(s):

Adam J. Saffer ◽

Andrew Pilny ◽

Erich J. Sommerfeldt

Keyword(s):

Communication Network ◽

Civil Society ◽

Real World ◽

Network Formation ◽

Preferential Attachment ◽

Interorganizational Networks ◽

Communication Research ◽

Relationship Formation ◽

Interorganizational Communication ◽

Strength Of Ties

Recent interorganizational communication research has taken up the question: why are networks structured the way they are? This line of inquiry has advanced communication network research by helping explain how and why networks take on certain structures or why certain organizations become positioned advantageously (or not). Yet, those studies assume relationships among organizations are either present or absent. This study considers how the strength of ties and multiplex relationships among organizations may reveal a more complex explanation for why networks take on certain structures. Our results challenge some long held assumptions about the mechanisms that influence network formation. For instance, our results offer important insights into the consequences of closure mechanisms, the applicability of preferential attachment to real-world networks, and the nuances of homophily in network formation on multidimensional relationships in a communication network. Implications for interorganizational networks are discussed.

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