Nonlinear growth in weighted networks with neighborhood preferential attachment

2012 ◽  
Vol 391 (20) ◽  
pp. 4790-4797 ◽  
Author(s):  
Yikang Rui ◽  
Yifang Ban
Keyword(s):  
Preferential Attachment ◽  
Weighted Networks ◽  
Nonlinear Growth

scholarly journals Scale free is not rare in international trade networks

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.

scholarly journals Nonlinear growth: an origin of hub organization in complex networks

2017 ◽  
Vol 4 (3) ◽  
pp. 160691 ◽  
Author(s):  
Roman Bauer ◽  
Marcus Kaiser
Keyword(s):  
Real World ◽  
Preferential Attachment ◽  
Fibre Tract ◽  
Nonlinear Network ◽  
Network Function ◽  
Nonlinear Growth ◽  
Rich Club ◽  
Spreading Dynamics ◽  
Classical Models ◽  
Generic Representation

Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein–protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication–divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.

A WEIGHTED NETWORK EVOLUTION WITH TRAFFIC FLOW UNDER PREFERENTIAL ATTACHMENT

2013 ◽  
Vol 24 (04) ◽  
pp. 1350020 ◽  
Author(s):  
MEIFENG DAI ◽  
RONGRONG LU
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Traditional Method ◽  
Preferential Attachment ◽  
Transportation System ◽  
Network Evolution ◽  
Random Component ◽  
Weighted Network ◽  
Scale Free ◽  
Weighted Networks

With more attentions drawn to transportation, we find that it is available to analyze transportation system by using weighted networks. Distinguishing from traditional method, we introduce a dynamic weighted network model with evolutional size and weight. Additionally, the expected strength allocation vector of the model consists of a deterministic component reflecting preferential attachment and a random component, which can characterize the realistic network better. In this paper, we obtain the degree and strength distributions of the weighted network and we conclude that the network has scale-free characteristics.

Nonlinear growth curve modeling using penalized spline models: A gentle introduction.

2019 ◽  
Vol 24 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Hye Won Suk ◽  
Stephen G. West ◽  
Kimberly L. Fine ◽  
Kevin J. Grimm
Keyword(s):  
Growth Curve ◽  
Growth Curve Modeling ◽  
Nonlinear Growth ◽  
Penalized Spline ◽  
Spline Models ◽  
Curve Modeling

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

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.

scholarly journals Nonlinear growth of the converging Richtmyer-Meshkov instability in a conventional shock tube

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Marc Vandenboomgaerde ◽  
Pascal Rouzier ◽  
Denis Souffland ◽  
Laurent Biamino ◽  
Georges Jourdan ◽  
...  
Keyword(s):  
Shock Tube ◽  
Nonlinear Growth

scholarly journals The structure of co-publications multilayer network

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.

scholarly journals Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

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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